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Published: 04 June 2021

Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews

Israel Júnior Borges do Nascimento 1 , 2 ,
Dónal P. O’Mathúna 3 , 4 ,
Thilo Caspar von Groote 5 ,
Hebatullah Mohamed Abdulazeem 6 ,
Ishanka Weerasekara 7 , 8 ,
Ana Marusic 9 ,
Livia Puljak ORCID: orcid.org/0000-0002-8467-6061 10 ,
Vinicius Tassoni Civile 11 ,
Irena Zakarija-Grkovic 9 ,
Tina Poklepovic Pericic 9 ,
Alvaro Nagib Atallah 11 ,
Santino Filoso 12 ,
Nicola Luigi Bragazzi 13 &
Milena Soriano Marcolino 1

On behalf of the International Network of Coronavirus Disease 2019 (InterNetCOVID-19)

BMC Infectious Diseases volume 21 , Article number: 525 ( 2021 ) Cite this article

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Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging, and ongoing critical appraisal of this output is essential. We aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO’s Global Research, LILACS, and Epistemonikos) were searched from December 1, 2019, to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory, and radiological findings), and quality assessment (AMSTAR 2). A meta-analysis was performed of the prevalence of clinical outcomes.

Eighteen systematic reviews were included; one was empty (did not identify any relevant study). Using AMSTAR 2, confidence in the results of all 18 reviews was rated as “critically low”. Identified symptoms of COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%) and gastrointestinal complaints (5–9%). Severe symptoms were more common in men. Elevated C-reactive protein and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. A frequent finding on chest imaging was uni- or bilateral multilobar ground-glass opacity. A single review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3 to 13.9%.

Conclusions

In this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic were of questionable usefulness. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards.

Peer Review reports

The spread of the “Severe Acute Respiratory Coronavirus 2” (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [ 1 ]. The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [ 2 ], causing massive economic strain, and escalating healthcare and public health expenses [ 3 , 4 ].

The research community has responded by publishing an impressive number of scientific reports related to COVID-19. The world was alerted to the new disease at the beginning of 2020 [ 1 ], and by mid-March 2020, more than 2000 articles had been published on COVID-19 in scholarly journals, with 25% of them containing original data [ 5 ]. The living map of COVID-19 evidence, curated by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), contained more than 40,000 records by February 2021 [ 6 ]. More than 100,000 records on PubMed were labeled as “SARS-CoV-2 literature, sequence, and clinical content” by February 2021 [ 7 ].

Due to publication speed, the research community has voiced concerns regarding the quality and reproducibility of evidence produced during the COVID-19 pandemic, warning of the potential damaging approach of “publish first, retract later” [ 8 ]. It appears that these concerns are not unfounded, as it has been reported that COVID-19 articles were overrepresented in the pool of retracted articles in 2020 [ 9 ]. These concerns about inadequate evidence are of major importance because they can lead to poor clinical practice and inappropriate policies [ 10 ].

Systematic reviews are a cornerstone of today’s evidence-informed decision-making. By synthesizing all relevant evidence regarding a particular topic, systematic reviews reflect the current scientific knowledge. Systematic reviews are considered to be at the highest level in the hierarchy of evidence and should be used to make informed decisions. However, with high numbers of systematic reviews of different scope and methodological quality being published, overviews of multiple systematic reviews that assess their methodological quality are essential [ 11 , 12 , 13 ]. An overview of systematic reviews helps identify and organize the literature and highlights areas of priority in decision-making.

In this overview of systematic reviews, we aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Methodology

Research question.

This overview’s primary objective was to summarize and critically appraise systematic reviews that assessed any type of primary clinical data from patients infected with SARS-CoV-2. Our research question was purposefully broad because we wanted to analyze as many systematic reviews as possible that were available early following the COVID-19 outbreak.

Study design

We conducted an overview of systematic reviews. The idea for this overview originated in a protocol for a systematic review submitted to PROSPERO (CRD42020170623), which indicated a plan to conduct an overview.

Overviews of systematic reviews use explicit and systematic methods for searching and identifying multiple systematic reviews addressing related research questions in the same field to extract and analyze evidence across important outcomes. Overviews of systematic reviews are in principle similar to systematic reviews of interventions, but the unit of analysis is a systematic review [ 14 , 15 , 16 ].

We used the overview methodology instead of other evidence synthesis methods to allow us to collate and appraise multiple systematic reviews on this topic, and to extract and analyze their results across relevant topics [ 17 ]. The overview and meta-analysis of systematic reviews allowed us to investigate the methodological quality of included studies, summarize results, and identify specific areas of available or limited evidence, thereby strengthening the current understanding of this novel disease and guiding future research [ 13 ].

A reporting guideline for overviews of reviews is currently under development, i.e., Preferred Reporting Items for Overviews of Reviews (PRIOR) [ 18 ]. As the PRIOR checklist is still not published, this study was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 statement [ 19 ]. The methodology used in this review was adapted from the Cochrane Handbook for Systematic Reviews of Interventions and also followed established methodological considerations for analyzing existing systematic reviews [ 14 ].

Approval of a research ethics committee was not necessary as the study analyzed only publicly available articles.

Eligibility criteria

Systematic reviews were included if they analyzed primary data from patients infected with SARS-CoV-2 as confirmed by RT-PCR or another pre-specified diagnostic technique. Eligible reviews covered all topics related to COVID-19 including, but not limited to, those that reported clinical symptoms, diagnostic methods, therapeutic interventions, laboratory findings, or radiological results. Both full manuscripts and abbreviated versions, such as letters, were eligible.

No restrictions were imposed on the design of the primary studies included within the systematic reviews, the last search date, whether the review included meta-analyses or language. Reviews related to SARS-CoV-2 and other coronaviruses were eligible, but from those reviews, we analyzed only data related to SARS-CoV-2.

No consensus definition exists for a systematic review [ 20 ], and debates continue about the defining characteristics of a systematic review [ 21 ]. Cochrane’s guidance for overviews of reviews recommends setting pre-established criteria for making decisions around inclusion [ 14 ]. That is supported by a recent scoping review about guidance for overviews of systematic reviews [ 22 ].

Thus, for this study, we defined a systematic review as a research report which searched for primary research studies on a specific topic using an explicit search strategy, had a detailed description of the methods with explicit inclusion criteria provided, and provided a summary of the included studies either in narrative or quantitative format (such as a meta-analysis). Cochrane and non-Cochrane systematic reviews were considered eligible for inclusion, with or without meta-analysis, and regardless of the study design, language restriction and methodology of the included primary studies. To be eligible for inclusion, reviews had to be clearly analyzing data related to SARS-CoV-2 (associated or not with other viruses). We excluded narrative reviews without those characteristics as these are less likely to be replicable and are more prone to bias.

Scoping reviews and rapid reviews were eligible for inclusion in this overview if they met our pre-defined inclusion criteria noted above. We included reviews that addressed SARS-CoV-2 and other coronaviruses if they reported separate data regarding SARS-CoV-2.

Information sources

Nine databases were searched for eligible records published between December 1, 2019, and March 24, 2020: Cochrane Database of Systematic Reviews via Cochrane Library, PubMed, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Sciences, LILACS (Latin American and Caribbean Health Sciences Literature), PDQ-Evidence, WHO’s Global Research on Coronavirus Disease (COVID-19), and Epistemonikos.

The comprehensive search strategy for each database is provided in Additional file 1 and was designed and conducted in collaboration with an information specialist. All retrieved records were primarily processed in EndNote, where duplicates were removed, and records were then imported into the Covidence platform [ 23 ]. In addition to database searches, we screened reference lists of reviews included after screening records retrieved via databases.

Study selection

All searches, screening of titles and abstracts, and record selection, were performed independently by two investigators using the Covidence platform [ 23 ]. Articles deemed potentially eligible were retrieved for full-text screening carried out independently by two investigators. Discrepancies at all stages were resolved by consensus. During the screening, records published in languages other than English were translated by a native/fluent speaker.

Data collection process

We custom designed a data extraction table for this study, which was piloted by two authors independently. Data extraction was performed independently by two authors. Conflicts were resolved by consensus or by consulting a third researcher.

We extracted the following data: article identification data (authors’ name and journal of publication), search period, number of databases searched, population or settings considered, main results and outcomes observed, and number of participants. From Web of Science (Clarivate Analytics, Philadelphia, PA, USA), we extracted journal rank (quartile) and Journal Impact Factor (JIF).

We categorized the following as primary outcomes: all-cause mortality, need for and length of mechanical ventilation, length of hospitalization (in days), admission to intensive care unit (yes/no), and length of stay in the intensive care unit.

The following outcomes were categorized as exploratory: diagnostic methods used for detection of the virus, male to female ratio, clinical symptoms, pharmacological and non-pharmacological interventions, laboratory findings (full blood count, liver enzymes, C-reactive protein, d-dimer, albumin, lipid profile, serum electrolytes, blood vitamin levels, glucose levels, and any other important biomarkers), and radiological findings (using radiography, computed tomography, magnetic resonance imaging or ultrasound).

We also collected data on reporting guidelines and requirements for the publication of systematic reviews and meta-analyses from journal websites where included reviews were published.

Quality assessment in individual reviews

Two researchers independently assessed the reviews’ quality using the “A MeaSurement Tool to Assess Systematic Reviews 2 (AMSTAR 2)”. We acknowledge that the AMSTAR 2 was created as “a critical appraisal tool for systematic reviews that include randomized or non-randomized studies of healthcare interventions, or both” [ 24 ]. However, since AMSTAR 2 was designed for systematic reviews of intervention trials, and we included additional types of systematic reviews, we adjusted some AMSTAR 2 ratings and reported these in Additional file 2 .

Adherence to each item was rated as follows: yes, partial yes, no, or not applicable (such as when a meta-analysis was not conducted). The overall confidence in the results of the review is rated as “critically low”, “low”, “moderate” or “high”, according to the AMSTAR 2 guidance based on seven critical domains, which are items 2, 4, 7, 9, 11, 13, 15 as defined by AMSTAR 2 authors [ 24 ]. We reported our adherence ratings for transparency of our decision with accompanying explanations, for each item, in each included review.

One of the included systematic reviews was conducted by some members of this author team [ 25 ]. This review was initially assessed independently by two authors who were not co-authors of that review to prevent the risk of bias in assessing this study.

Synthesis of results

For data synthesis, we prepared a table summarizing each systematic review. Graphs illustrating the mortality rate and clinical symptoms were created. We then prepared a narrative summary of the methods, findings, study strengths, and limitations.

For analysis of the prevalence of clinical outcomes, we extracted data on the number of events and the total number of patients to perform proportional meta-analysis using RStudio© software, with the “meta” package (version 4.9–6), using the “metaprop” function for reviews that did not perform a meta-analysis, excluding case studies because of the absence of variance. For reviews that did not perform a meta-analysis, we presented pooled results of proportions with their respective confidence intervals (95%) by the inverse variance method with a random-effects model, using the DerSimonian-Laird estimator for τ 2 . We adjusted data using Freeman-Tukey double arcosen transformation. Confidence intervals were calculated using the Clopper-Pearson method for individual studies. We created forest plots using the RStudio© software, with the “metafor” package (version 2.1–0) and “forest” function.

Managing overlapping systematic reviews

Some of the included systematic reviews that address the same or similar research questions may include the same primary studies in overviews. Including such overlapping reviews may introduce bias when outcome data from the same primary study are included in the analyses of an overview multiple times. Thus, in summaries of evidence, multiple-counting of the same outcome data will give data from some primary studies too much influence [ 14 ]. In this overview, we did not exclude overlapping systematic reviews because, according to Cochrane’s guidance, it may be appropriate to include all relevant reviews’ results if the purpose of the overview is to present and describe the current body of evidence on a topic [ 14 ]. To avoid any bias in summary estimates associated with overlapping reviews, we generated forest plots showing data from individual systematic reviews, but the results were not pooled because some primary studies were included in multiple reviews.

Our search retrieved 1063 publications, of which 175 were duplicates. Most publications were excluded after the title and abstract analysis ( n = 860). Among the 28 studies selected for full-text screening, 10 were excluded for the reasons described in Additional file 3 , and 18 were included in the final analysis (Fig. 1 ) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Reference list screening did not retrieve any additional systematic reviews.

PRISMA flow diagram

Characteristics of included reviews

Summary features of 18 systematic reviews are presented in Table 1 . They were published in 14 different journals. Only four of these journals had specific requirements for systematic reviews (with or without meta-analysis): European Journal of Internal Medicine, Journal of Clinical Medicine, Ultrasound in Obstetrics and Gynecology, and Clinical Research in Cardiology . Two journals reported that they published only invited reviews ( Journal of Medical Virology and Clinica Chimica Acta ). Three systematic reviews in our study were published as letters; one was labeled as a scoping review and another as a rapid review (Table 2 ).

All reviews were published in English, in first quartile (Q1) journals, with JIF ranging from 1.692 to 6.062. One review was empty, meaning that its search did not identify any relevant studies; i.e., no primary studies were included [ 36 ]. The remaining 17 reviews included 269 unique studies; the majority ( N = 211; 78%) were included in only a single review included in our study (range: 1 to 12). Primary studies included in the reviews were published between December 2019 and March 18, 2020, and comprised case reports, case series, cohorts, and other observational studies. We found only one review that included randomized clinical trials [ 38 ]. In the included reviews, systematic literature searches were performed from 2019 (entire year) up to March 9, 2020. Ten systematic reviews included meta-analyses. The list of primary studies found in the included systematic reviews is shown in Additional file 4 , as well as the number of reviews in which each primary study was included.

Population and study designs

Most of the reviews analyzed data from patients with COVID-19 who developed pneumonia, acute respiratory distress syndrome (ARDS), or any other correlated complication. One review aimed to evaluate the effectiveness of using surgical masks on preventing transmission of the virus [ 36 ], one review was focused on pediatric patients [ 34 ], and one review investigated COVID-19 in pregnant women [ 37 ]. Most reviews assessed clinical symptoms, laboratory findings, or radiological results.

Systematic review findings

The summary of findings from individual reviews is shown in Table 2 . Overall, all-cause mortality ranged from 0.3 to 13.9% (Fig. 2 ).

A meta-analysis of the prevalence of mortality

Clinical symptoms

Seven reviews described the main clinical manifestations of COVID-19 [ 26 , 28 , 29 , 34 , 35 , 39 , 41 ]. Three of them provided only a narrative discussion of symptoms [ 26 , 34 , 35 ]. In the reviews that performed a statistical analysis of the incidence of different clinical symptoms, symptoms in patients with COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%), gastrointestinal disorders, such as diarrhea, nausea or vomiting (5.0–9.0%), and others (including, in one study only: dizziness 12.1%) (Figs. 3 , 4 , 5 , 6 , 7 , 8 and 9 ). Three reviews assessed cough with and without sputum together; only one review assessed sputum production itself (28.5%).

A meta-analysis of the prevalence of fever

A meta-analysis of the prevalence of cough

A meta-analysis of the prevalence of dyspnea

A meta-analysis of the prevalence of fatigue or myalgia

A meta-analysis of the prevalence of headache

A meta-analysis of the prevalence of gastrointestinal disorders

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

Three reviews described methodologies, protocols, and tools used for establishing the diagnosis of COVID-19 [ 26 , 34 , 38 ]. The use of respiratory swabs (nasal or pharyngeal) or blood specimens to assess the presence of SARS-CoV-2 nucleic acid using RT-PCR assays was the most commonly used diagnostic method mentioned in the included studies. These diagnostic tests have been widely used, but their precise sensitivity and specificity remain unknown. One review included a Chinese study with clinical diagnosis with no confirmation of SARS-CoV-2 infection (patients were diagnosed with COVID-19 if they presented with at least two symptoms suggestive of COVID-19, together with laboratory and chest radiography abnormalities) [ 34 ].

Therapeutic possibilities

Pharmacological and non-pharmacological interventions (supportive therapies) used in treating patients with COVID-19 were reported in five reviews [ 25 , 27 , 34 , 35 , 38 ]. Antivirals used empirically for COVID-19 treatment were reported in seven reviews [ 25 , 27 , 34 , 35 , 37 , 38 , 41 ]; most commonly used were protease inhibitors (lopinavir, ritonavir, darunavir), nucleoside reverse transcriptase inhibitor (tenofovir), nucleotide analogs (remdesivir, galidesivir, ganciclovir), and neuraminidase inhibitors (oseltamivir). Umifenovir, a membrane fusion inhibitor, was investigated in two studies [ 25 , 35 ]. Possible supportive interventions analyzed were different types of oxygen supplementation and breathing support (invasive or non-invasive ventilation) [ 25 ]. The use of antibiotics, both empirically and to treat secondary pneumonia, was reported in six studies [ 25 , 26 , 27 , 34 , 35 , 38 ]. One review specifically assessed evidence on the efficacy and safety of the anti-malaria drug chloroquine [ 27 ]. It identified 23 ongoing trials investigating the potential of chloroquine as a therapeutic option for COVID-19, but no verifiable clinical outcomes data. The use of mesenchymal stem cells, antifungals, and glucocorticoids were described in four reviews [ 25 , 34 , 35 , 38 ].

Laboratory and radiological findings

Of the 18 reviews included in this overview, eight analyzed laboratory parameters in patients with COVID-19 [ 25 , 29 , 30 , 32 , 33 , 34 , 35 , 39 ]; elevated C-reactive protein levels, associated with lymphocytopenia, elevated lactate dehydrogenase, as well as slightly elevated aspartate and alanine aminotransferase (AST, ALT) were commonly described in those eight reviews. Lippi et al. assessed cardiac troponin I (cTnI) [ 25 ], procalcitonin [ 32 ], and platelet count [ 33 ] in COVID-19 patients. Elevated levels of procalcitonin [ 32 ] and cTnI [ 30 ] were more likely to be associated with a severe disease course (requiring intensive care unit admission and intubation). Furthermore, thrombocytopenia was frequently observed in patients with complicated COVID-19 infections [ 33 ].

Chest imaging (chest radiography and/or computed tomography) features were assessed in six reviews, all of which described a frequent pattern of local or bilateral multilobar ground-glass opacity [ 25 , 34 , 35 , 39 , 40 , 41 ]. Those six reviews showed that septal thickening, bronchiectasis, pleural and cardiac effusions, halo signs, and pneumothorax were observed in patients suffering from COVID-19.

Quality of evidence in individual systematic reviews

Table 3 shows the detailed results of the quality assessment of 18 systematic reviews, including the assessment of individual items and summary assessment. A detailed explanation for each decision in each review is available in Additional file 5 .

Using AMSTAR 2 criteria, confidence in the results of all 18 reviews was rated as “critically low” (Table 3 ). Common methodological drawbacks were: omission of prospective protocol submission or publication; use of inappropriate search strategy: lack of independent and dual literature screening and data-extraction (or methodology unclear); absence of an explanation for heterogeneity among the studies included; lack of reasons for study exclusion (or rationale unclear).

Risk of bias assessment, based on a reported methodological tool, and quality of evidence appraisal, in line with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method, were reported only in one review [ 25 ]. Five reviews presented a table summarizing bias, using various risk of bias tools [ 25 , 29 , 39 , 40 , 41 ]. One review analyzed “study quality” [ 37 ]. One review mentioned the risk of bias assessment in the methodology but did not provide any related analysis [ 28 ].

This overview of systematic reviews analyzed the first 18 systematic reviews published after the onset of the COVID-19 pandemic, up to March 24, 2020, with primary studies involving more than 60,000 patients. Using AMSTAR-2, we judged that our confidence in all those reviews was “critically low”. Ten reviews included meta-analyses. The reviews presented data on clinical manifestations, laboratory and radiological findings, and interventions. We found no systematic reviews on the utility of diagnostic tests.

Symptoms were reported in seven reviews; most of the patients had a fever, cough, dyspnea, myalgia or muscle fatigue, and gastrointestinal disorders such as diarrhea, nausea, or vomiting. Olfactory dysfunction (anosmia or dysosmia) has been described in patients infected with COVID-19 [ 43 ]; however, this was not reported in any of the reviews included in this overview. During the SARS outbreak in 2002, there were reports of impairment of the sense of smell associated with the disease [ 44 , 45 ].

The reported mortality rates ranged from 0.3 to 14% in the included reviews. Mortality estimates are influenced by the transmissibility rate (basic reproduction number), availability of diagnostic tools, notification policies, asymptomatic presentations of the disease, resources for disease prevention and control, and treatment facilities; variability in the mortality rate fits the pattern of emerging infectious diseases [ 46 ]. Furthermore, the reported cases did not consider asymptomatic cases, mild cases where individuals have not sought medical treatment, and the fact that many countries had limited access to diagnostic tests or have implemented testing policies later than the others. Considering the lack of reviews assessing diagnostic testing (sensitivity, specificity, and predictive values of RT-PCT or immunoglobulin tests), and the preponderance of studies that assessed only symptomatic individuals, considerable imprecision around the calculated mortality rates existed in the early stage of the COVID-19 pandemic.

Few reviews included treatment data. Those reviews described studies considered to be at a very low level of evidence: usually small, retrospective studies with very heterogeneous populations. Seven reviews analyzed laboratory parameters; those reviews could have been useful for clinicians who attend patients suspected of COVID-19 in emergency services worldwide, such as assessing which patients need to be reassessed more frequently.

All systematic reviews scored poorly on the AMSTAR 2 critical appraisal tool for systematic reviews. Most of the original studies included in the reviews were case series and case reports, impacting the quality of evidence. Such evidence has major implications for clinical practice and the use of these reviews in evidence-based practice and policy. Clinicians, patients, and policymakers can only have the highest confidence in systematic review findings if high-quality systematic review methodologies are employed. The urgent need for information during a pandemic does not justify poor quality reporting.

We acknowledge that there are numerous challenges associated with analyzing COVID-19 data during a pandemic [ 47 ]. High-quality evidence syntheses are needed for decision-making, but each type of evidence syntheses is associated with its inherent challenges.

The creation of classic systematic reviews requires considerable time and effort; with massive research output, they quickly become outdated, and preparing updated versions also requires considerable time. A recent study showed that updates of non-Cochrane systematic reviews are published a median of 5 years after the publication of the previous version [ 48 ].

Authors may register a review and then abandon it [ 49 ], but the existence of a public record that is not updated may lead other authors to believe that the review is still ongoing. A quarter of Cochrane review protocols remains unpublished as completed systematic reviews 8 years after protocol publication [ 50 ].

Rapid reviews can be used to summarize the evidence, but they involve methodological sacrifices and simplifications to produce information promptly, with inconsistent methodological approaches [ 51 ]. However, rapid reviews are justified in times of public health emergencies, and even Cochrane has resorted to publishing rapid reviews in response to the COVID-19 crisis [ 52 ]. Rapid reviews were eligible for inclusion in this overview, but only one of the 18 reviews included in this study was labeled as a rapid review.

Ideally, COVID-19 evidence would be continually summarized in a series of high-quality living systematic reviews, types of evidence synthesis defined as “ a systematic review which is continually updated, incorporating relevant new evidence as it becomes available ” [ 53 ]. However, conducting living systematic reviews requires considerable resources, calling into question the sustainability of such evidence synthesis over long periods [ 54 ].

Research reports about COVID-19 will contribute to research waste if they are poorly designed, poorly reported, or simply not necessary. In principle, systematic reviews should help reduce research waste as they usually provide recommendations for further research that is needed or may advise that sufficient evidence exists on a particular topic [ 55 ]. However, systematic reviews can also contribute to growing research waste when they are not needed, or poorly conducted and reported. Our present study clearly shows that most of the systematic reviews that were published early on in the COVID-19 pandemic could be categorized as research waste, as our confidence in their results is critically low.

Our study has some limitations. One is that for AMSTAR 2 assessment we relied on information available in publications; we did not attempt to contact study authors for clarifications or additional data. In three reviews, the methodological quality appraisal was challenging because they were published as letters, or labeled as rapid communications. As a result, various details about their review process were not included, leading to AMSTAR 2 questions being answered as “not reported”, resulting in low confidence scores. Full manuscripts might have provided additional information that could have led to higher confidence in the results. In other words, low scores could reflect incomplete reporting, not necessarily low-quality review methods. To make their review available more rapidly and more concisely, the authors may have omitted methodological details. A general issue during a crisis is that speed and completeness must be balanced. However, maintaining high standards requires proper resourcing and commitment to ensure that the users of systematic reviews can have high confidence in the results.

Furthermore, we used adjusted AMSTAR 2 scoring, as the tool was designed for critical appraisal of reviews of interventions. Some reviews may have received lower scores than actually warranted in spite of these adjustments.

Another limitation of our study may be the inclusion of multiple overlapping reviews, as some included reviews included the same primary studies. According to the Cochrane Handbook, including overlapping reviews may be appropriate when the review’s aim is “ to present and describe the current body of systematic review evidence on a topic ” [ 12 ], which was our aim. To avoid bias with summarizing evidence from overlapping reviews, we presented the forest plots without summary estimates. The forest plots serve to inform readers about the effect sizes for outcomes that were reported in each review.

Several authors from this study have contributed to one of the reviews identified [ 25 ]. To reduce the risk of any bias, two authors who did not co-author the review in question initially assessed its quality and limitations.

Finally, we note that the systematic reviews included in our overview may have had issues that our analysis did not identify because we did not analyze their primary studies to verify the accuracy of the data and information they presented. We give two examples to substantiate this possibility. Lovato et al. wrote a commentary on the review of Sun et al. [ 41 ], in which they criticized the authors’ conclusion that sore throat is rare in COVID-19 patients [ 56 ]. Lovato et al. highlighted that multiple studies included in Sun et al. did not accurately describe participants’ clinical presentations, warning that only three studies clearly reported data on sore throat [ 56 ].

In another example, Leung [ 57 ] warned about the review of Li, L.Q. et al. [ 29 ]: “ it is possible that this statistic was computed using overlapped samples, therefore some patients were double counted ”. Li et al. responded to Leung that it is uncertain whether the data overlapped, as they used data from published articles and did not have access to the original data; they also reported that they requested original data and that they plan to re-do their analyses once they receive them; they also urged readers to treat the data with caution [ 58 ]. This points to the evolving nature of evidence during a crisis.

Our study’s strength is that this overview adds to the current knowledge by providing a comprehensive summary of all the evidence synthesis about COVID-19 available early after the onset of the pandemic. This overview followed strict methodological criteria, including a comprehensive and sensitive search strategy and a standard tool for methodological appraisal of systematic reviews.

In conclusion, in this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all the reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic could be categorized as research waste. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards to provide patients, clinicians, and decision-makers trustworthy evidence.

Availability of data and materials

All data collected and analyzed within this study are available from the corresponding author on reasonable request.

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Acknowledgments

We thank Catherine Henderson DPhil from Swanscoe Communications for pro bono medical writing and editing support. We acknowledge support from the Covidence Team, specifically Anneliese Arno. We thank the whole International Network of Coronavirus Disease 2019 (InterNetCOVID-19) for their commitment and involvement. Members of the InterNetCOVID-19 are listed in Additional file 6 . We thank Pavel Cerny and Roger Crosthwaite for guiding the team supervisor (IJBN) on human resources management.

This research received no external funding.

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University Hospital and School of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

Israel Júnior Borges do Nascimento & Milena Soriano Marcolino

Medical College of Wisconsin, Milwaukee, WI, USA

Israel Júnior Borges do Nascimento

Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA

Dónal P. O’Mathúna

School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland

Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany

Thilo Caspar von Groote

Department of Sport and Health Science, Technische Universität München, Munich, Germany

Hebatullah Mohamed Abdulazeem

School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, Australia

Ishanka Weerasekara

Department of Physiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka

Cochrane Croatia, University of Split, School of Medicine, Split, Croatia

Ana Marusic, Irena Zakarija-Grkovic & Tina Poklepovic Pericic

Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000, Zagreb, Croatia

Livia Puljak

Cochrane Brazil, Evidence-Based Health Program, Universidade Federal de São Paulo, São Paulo, Brazil

Vinicius Tassoni Civile & Alvaro Nagib Atallah

Yorkville University, Fredericton, New Brunswick, Canada

Santino Filoso

Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada

Nicola Luigi Bragazzi

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IJBN conceived the research idea and worked as a project coordinator. DPOM, TCVG, HMA, IW, AM, LP, VTC, IZG, TPP, ANA, SF, NLB and MSM were involved in data curation, formal analysis, investigation, methodology, and initial draft writing. All authors revised the manuscript critically for the content. The author(s) read and approved the final manuscript.

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Correspondence to Livia Puljak .

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

Adjusted scoring of AMSTAR 2 used in this study for systematic reviews of studies that did not analyze interventions.

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

Table of overlapping studies, containing the list of primary studies included, their visual overlap in individual systematic reviews, and the number in how many reviews each primary study was included.

Additional file 5: Appendix 5.

A detailed explanation of AMSTAR scoring for each item in each review.

Additional file 6: Appendix 6.

List of members and affiliates of International Network of Coronavirus Disease 2019 (InterNetCOVID-19).

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Borges do Nascimento, I.J., O’Mathúna, D.P., von Groote, T.C. et al. Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews. BMC Infect Dis 21 , 525 (2021). https://doi.org/10.1186/s12879-021-06214-4

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100+ Best Research Titles About COVID-19 Examples

The covid-19 pandemic has been the most devastating thing to happen in humanity in the past decade or two. It caused global panic and changed people’s lives in multiple aspects. Therefore, it is the perfect research topic for high school, postgraduate, and undergraduate students.

Exciting Sample Research Title About Pandemic

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Academic research related to covid-19 would be perfect because of its relevance. Furthermore, it applies to any field of study thanks to the vast and immense impacts of the pandemic. For instance, business and finance students can research the effects of the covid-19 pandemic on the economy, while social science majors can discuss the various social results.

The pandemic research topics are a good path also because they are interesting. Additionally, research topic examples about covid-19 give you a great research opportunity because of the numerous materials. There are multiple topics you can consider, from the quantitative and qualitative research titles about covid 19 to the effects and reception of the vaccine, among others.

Ready for detailed quantitative and qualitative research topics ? Find a great research title about a covid-19 example from the samples below.

The impacts of the pandemic were and are still felt globally. So, this means that there are numerous creative directions you can pursue when choosing the perfect topic. Here are some research titles about the pandemic and argumentative essay topics :

An exploration of the impacts of the pandemic on the global economy
The covid-19 pandemic and the global recession: what is the link?
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Quantitative research about the pandemic involves collecting and analyzing data. However, choosing a quantitative research topic is not easy since you must select a researchable one. An example of a quantitative research title about covid-19 may be a good start. So, let’s look at some quantitative research title examples about covid-19:

How effective are detergents against germs during the pandemic?
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A qualitative research title about covid-19 significantly depends on data collected from first-hand observations, interviews, recordings in natural settings, and case studies. So, qualitative social issues research topics are mostly non-numerical data. Find a qualitative research title about the pandemic from the samples below:

How ethical are the covid-19 regulations?
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You can also find a quantitative research title about covid-19, specifically focusing on the pandemic and its resulting issues. In addition to a quantitative research topic during a pandemic, research topics for STEM students are also pretty interesting. Here are some research topics during the pandemic that you can write about:

A link between the pandemic and employee retention rates in large corporations
Global recovery from the pandemic
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How the global economy can recover from the pandemic
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The difference between the acceptance of the covid-19 vaccine in first and third-world countries
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When choosing a research topic, always pick an interesting and relevant topic. Doing so will simplify your research, help with data collection, and make your paper enjoyable. Get a research title about covid 19 quantitative for 2020 from the list below:

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Quarantine in third-world countries compared to first-world countries
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Social barriers during and after the coronavirus pandemic
Consumer Psychonomic during the covid-19 pandemic
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The covid-19 pandemic offers multiple incredible research topic ideas. Choosing the best research title about the coronavirus can be tricky. So, let’s look at some qualitative research title examples about covid-19:

The covid-19 pandemic and what we can learn from it
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The pandemic is a great study area for a thesis. You can choose various directions for your thesis depending on your study area and interest. Whether it is a quantitative research title about the pandemic or an example of a qualitative research title about covid-19, the following research titles about covid 19 should come in handy:

The coronavirus pandemic: changes in public spaces and hygiene
Development Control Regulations as the perfect medium to navigate and fight the pandemic
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The rate of business launches during the pandemic
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A research title about the pandemic can be a great idea if you want to study a relevant topic. However, the topic relevance will depend on your study area. Find a great topic for research this pandemic from the list below:

A comprehensive reflection on the covid-19 pandemic
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A research title about the vaccine of covid 19 can be controversial. However, it makes an excellent topic for intellectual study. Find the best title for research about the pandemic related to vaccines

Mental health during the coronavirus pandemic and what to improve
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You can find a thesis statement about social media or a great research title about covid 19 vaccine and other topics online. However, not every research title about covid is relevant or great for academic research. You need the best social media research topics . Find a fantastic title of research about covid from the list below:

How social media helped mitigate the impacts of the pandemic
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Social media influence during the pandemic and the changes
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Third-world countries like the Philippines are among the most impacted nations by the pandemic. So, cover the research title example quantitative or qualitative, depending on your preferred data collection and analysis techniques. Some pandemic research title examples about the Philippines are:

The Philippines’ medical sector during the pandemic
Mitigation measures by the Philippines government during the pandemic
How the pandemic impacted the Philippines’ public sector
The Philippines’ education sector after the pandemic
Religion and the covid-19 pandemic: God’s existence in Covid-19 times
Philippines’ public policies after the pandemic
The Philippines food and beverage plan: the impacts of the pandemic
Covid-19 vaccination rates in the Philippines’
The psychological impacts of the pandemic on the Philippines society
A survey on conditions of low-income households during the pandemic

Title research about the pandemic will earn you excellent grades because of the topic’s relevance and multiple study opportunities. However, the quality of the subject matters significantly. Find an example of a research title about covid-19 pandemic below:

What has the world learned from the covid-19 pandemic?
How has the pandemic influenced the public’s view of health?
Why are there fewer medical employees after the pandemic?
How did nurses and doctors survive overworking during the pandemic?
Is there a link between the global recession and the pandemic?
How did the WHO’s response to the pandemic help mitigate its impacts?
What challenges did the WHO face while addressing the covid-19 pandemic?
Should people continue getting covid-19 vaccinations in 2022?
What is the correlation between the pandemic and the current state of global society?
What is social solidarity during the pandemic?

The covid-19 pandemic front liners were among the most impacted by the pandemic. So, it would make sense to focus your study on the frontliners. Find an incredible sample of a research title during the pandemic here:

Frontliners during the pandemic: how were they affected?
An overview of front liner’s view of the pandemic
A look into the covid-19 pandemic through the eyes of the pandemic
School closures during the pandemic: the impacts on frontline families
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Funada S , Yoshioka T , Luo Y, et al. Global Trends in Highly Cited Studies in COVID-19 Research. JAMA Netw Open. 2023;6(9):e2332802. doi:10.1001/jamanetworkopen.2023.32802

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Global Trends in Highly Cited Studies in COVID-19 Research

1 Department of Health Promotion and Human Behavior, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
2 Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
3 Population Health and Policy Research Unit, Medical Education Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
4 Office of Evidence and Analysis, Japan Science and Technology Agency, Tokyo, Japan
5 Division of Surveillance and Policy Evaluation, National Cancer Center Institute for Cancer Control, Tokyo, Japan
Correction Incorrect Institution Name in Author Affiliations JAMA Network Open

Question What is the global trend of highly cited studies investigating COVID-19 since its outbreak?

Findings This cross-sectional study found that the number of highly cited studies peaked at 1292 studies at the end of 2021 and declined to 649 studies at the end of 2022. Highly cited studies from China showed a decreasing trend, while those from the US and UK showed an increasing trend.

Meaning These findings suggest that as the COVID-19 pandemic evolved in the 3 years since its outbreak, there were important shifts in trends of the number and origin of high-profile COVID-19 studies.

Importance Since the onset of the COVID-19 outbreak, an extremely high number of studies have been published worldwide, with variable quality. Research trends of highly cited papers may enable identification of influential research, providing insights for new research ideas; it is therefore important to investigate trends and focus on more influential publications in COVID-19–related studies.

Objective To examine research trends of highly cited studies by conducting a bibliometric analysis of highly cited studies in the previous 2 months about COVID-19.

Design, Setting, and Participants In this cross-sectional study, Essential Science Indicators (ESI) and Web of Science (WOS) Core Collection were used to find studies with a focus on COVID-19 that were identified as highly cited studies from Clarivate Analytics. Highly cited studies were extracted from the ESI database bimonthly between January 2020 and December 2022. Bibliographic details were extracted from WOS and combined with ESI data using unique accession numbers. The number of highly cited studies was counted based on the fractional counting method. Data were analyzed from January through July 2023.

Main Outcomes and Measures The number of publications by research field, country, and institutional affiliation.

Results The number of published COVID-19–related highly cited studies was 14 studies in January to February 2020, peaked at 1292 studies in November to December 2021, and showed a downward trend thereafter, reaching 649 studies in November to December 2022. China had the highest number of highly cited studies per 2-month period until July to August 2020 (138.3 studies vs 103.7 studies for the US, the second highest country), and the US had the greatest number of highly cited studies afterward (159.9 studies vs 157.6 studies for China in September to October 2020). Subsequently, the number of highly cited studies per 2-month period published by China declined (decreasing from 179.7 studies in November to December 2020 to 40.7 studies in September to October 2022), and the UK produced the second largest number of such studies in May to June 2021 (171.3 studies). Similarly, the top 5 institutional affiliations in May to June 2020 by highly cited studies per 2-month period were from China (Huazhong University: 14.7 studies; University of Hong Kong: 6.8 studies; Wuhan University: 4.8 studies; Zhejiang University: 4.5 studies; Fudan University: 4.5 studies), while in November to December 2022, the top 5 institutions were in the US and UK (Harvard University: 15.0 studies; University College London: 11.0 studies; University of Oxford: 10.2 studies; University of London: 9.9 studies; Imperial College London: 5.8 studies).

Conclusions and Relevance This study found that the total number of highly cited studies related to COVID-19 peaked at the end of 2021 and showed a downward trend until the end of 2022, while the origin of these studies shifted from China to the US and UK.

Since the outbreak of COVID-19 in December 2019, numerous studies have been conducted and published worldwide in response to the pandemic. 1 This trend may have been amplified by the use of preprint systems, such as medRxiv 2 and bioRxiv, 3 during the pandemic, as well as by the proliferation of predatory journals. 4 As a result, the total number of COVID-19–related publications, including preprints, has increased dramatically and now exceeds 350 000 studies. 5 The dissemination of COVID-19 research is highly active and constantly evolving. In such an expanding research environment, investigating research trends may help identify knowledge gaps and provide insightful research directions. 6 In addition, comparing trends across countries and institutional affiliations may support scientific policy and research management. 7

However, as previously found in 2020, 8 the increase in COVID-19–related publications has not necessarily been associated with increased high-quality evidence, and this concern has become a reality in 2023. A citation analysis of studies published in predatory journals found that 60% of publications had not attracted any citations and 38% were cited only up to 10 times. 9 The COVID-19 pandemic has seemingly been associated with an exacerbated issue of waste of studies (ie, doing unnecessary or poorly designed studies), 10 making the proper assessment and synthesis of research trends in COVID-19 research challenging. Therefore, some filtering system may be essential to efficiently narrow down desired publications from the vast collection and ensure that relevant and valuable studies are selected.

One way to address this challenge is to analyze highly cited studies, or hot papers, which refers to studies published within the previous 2 years that have received a considerable number of citations in the previous 2 months, placing them in the top 0.1% of studies in the same field. 11 High citation counts indicate that these studies have garnered significant attention from researchers. Furthermore, the list is updated every 2 months, allowing researchers to keep up with the latest trends and analyze them over time to capture shifts in the research landscape. Examining research trends of highly cited studies may allow the identification of influential studies, providing valuable insights for generating new research ideas. Bibliometrics is a scientific domain focused on measuring and quantifying various features in publications by examining the productivity of researchers, affiliations, and countries in specific fields. 12 Therefore, a bibliometric analysis may be appropriate for examining features of highly cited studies in COVID-19 research. To our knowledge, there have been no studies analyzing the trend of COVID-19–related highly cited studies.

This study aimed to investigate research trends of highly cited studies by conducting a bibliometric analysis of these studies on COVID-19 research. Additionally, by presenting these studies in chronological order, we aimed to identify changes in COVID-19 research trends.

This cross-sectional study was a bibliometric analysis of highly cited studies and followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline. According to the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan, institutional review board approval and participant consent were not required for this study because it used only published data.

We included all studies with a focus on COVID-19 identified as highly cited studies from Clarivate Analytics. We excluded studies that contained keywords related to COVID-19 in the text but did not investigate COVID-19. There was no restriction on the type of studies included.

This study used the following 5 selection steps for identifying highly cited studies on COVID-19. In step 1, we extracted a total of 18 periods of highly cited studies from the Essential Science Indicators (ESI) (Clarivate Analytics) database bimonthly from January 2020 to December 2022 (January to February 2020 through November to December 2020, January to February 2021 through November to December 2021, and January to February 2022 through November to December 2022). In step 2, based on a unique accession number associated with each highly cited study record in the ESI, we combined bibliographic details, such as abstract, document type, and others, from the Web of Science (WOS) Core Collection (Clarivate Analytics) with ESI data. The accession number is an identification number assigned to each study in WOS, and an individual study is identified by searching with the accession number in WOS. In step 3, we identified highly cited studies with a focus on COVID-19 from their titles and abstracts using the following search terms: “COVID-19” or “2019-nCoV” or “NOVEL 2019” or “CORONAVIRUS DISEASE 2019” or “SARS-COV-2” or “n-COV” or “COVID” or “CORONAVIRUS” or “SARS.” We excluded highly cited studies that did not contain predetermined keywords as non-COVID-19–related highly cited studies. In step 4, a total of 4 researchers (T.I., C.M., N.Y., and H.Y.) making pairs in rotation each independently reviewed titles, abstracts, and full texts and included COVID-19–related highly cited studies that fit eligibility criteria. Any disagreements or ambiguity between pairs were resolved through discussion or cross-check consultation with another researcher if required. In step 5, the same 4 researchers (T.I., C.M., N.Y., and H.Y.) checked how many duplicates were counted as highly cited studies between each period. We conducted this selection step once for each of 18 periods between January 2020 and December 2022.

We collected the following bibliographic information from the WOS database: titles, authors, corresponding authors, affiliations, publication journal, publication date, and research field. Based on this information, we used 3 variables to measure the trend of COVID-19–related research as follows: (1) The research fields variable included 22 ESI categories (agricultural science; biology and biochemistry; chemistry; clinical medicine; computer science; ecology/environment; economics and business; engineering; geosciences; immunology; materials science; mathematics; microbiology; molecular biology and genetics; multidisciplinary; neuroscience and behavior; pharmacology; physics; plant and animal science; psychiatry/psychology; social sciences, general; and space science). 13 Each journal is assigned to 1 field, and the research published in that journal adopts that field assignment. (2) The countries variable included countries of affiliation of all co-authors for each study. (3) The affiliations variable included affiliations of all co-authors for each study.

The bibliometric analysis descriptively summarizes the number of COVID-19–related highly cited studies. We counted the number of highly cited studies based on the fractional counting method. Compared with the full counting method, which counts the full number of each co-author and institutional affiliation, the fractional counting method had a fractional weight of each co-author and institutional affiliation, and each publication had a total weight of 1. 14 Highly cited study counts were compared between research fields, countries, and affiliations. As a sensitivity analysis, we performed the full counting method instead of the fractional counting method. We also performed the fractional counting method on countries and affiliations of corresponding authors as a sensitivity analysis. Data were analyzed using R statistical software version 4.3.1 (R Project for Statistical Computing). Data were analyzed from January through July 2023.

Figure 1 shows the selection step for highly cited studies on COVID-19 research. We identified 73 079 highly cited studies from the ESI database in 18 periods every 2 months between January 2020 and December 2022. From 73 079 highly cited studies, we excluded 57 236 highly cited studies by keyword search and 581 highly cited studies by title, abstract, and full text review. Finally, we identified 15 262 highly cited studies with duplicates and 4131 such studies without duplicates.

Figure 2 shows the number of highly cited studies for COVID-19 research in each period. The total number of highly cited studies exhibited gradual growth, from 3412 studies in January to February 2020 to 4389 studies in November to December 2022. Regarding COVID-19–related highly cited studies, the initial count was 14 studies in January to February 2020, increasing to 1292 studies in November- to December 2021. However, there was a subsequent decline to 649 studies in November to December 2022.

The top 10 research fields of highly cited studies of COVID-19 in each period are given in eFigure 1 in Supplement 1 . Although highly cited studies were predominantly from the clinical medicine field in January to February 2020 (9 of 14 studies [64.3%]), there was a gradual decrease in studies in this field starting in March to April 2022 (427 studies) until November to December 2022 (246 studies). Studies in other fields increased in number over time, with a particular increase in the fields of general social science, psychiatry and psychology, immunology, and molecular biology and genetics. For example, highly cited studies in general social science increased from 0 studies in January to February 2020 to 73 studies in July to August 2022.

Figure 3 shows the top 5 countries with the highest number of highly cited studies. China recorded the highest number of publications per 2-month period from January to February 2020 through July to August 2020 (138.3 studies), with the US following closely behind (103.7 studies during this period) and gradually increasing its output, overtaking China in September to October 2020 (159.9 studies vs 157.6 studies). China’s highly cited study output per 2-month period has been declining since November to December 2020 (decreasing from 179.7 studies in that period to 40.7 studies in September to October 2022), while there has been a steady increase in publications from the UK, increasing from 86.5 studies in November to December 2020 to ultimately overtake China in May to June 2021 (171.3 studies vs 166.6 studies). Starting in March to April 2022 until November to December 2022, the US, UK, and China had substantially reduced numbers of highly cited studies, and the downward trend continued until November to December 2022. The decrease in the number of highly cited studies per 2-month period from March to April 2022 to November to December 2022 was 366.8 studies to 190.6 studies for the US, 243.7 studies to 158.3 studies for the UK, and 107.5 studies to 45.5 studies for China. The trend remained the same using the full counting method (eFigure 2 in Supplement 1 ) and counting corresponding authors’ countries (eFigure 3 in Supplement 1 ) in sensitivity analyses.

Figure 4 shows the distribution of highly cited studies based on institutional affiliation across periods. Figure 4 A and Figure 4 B depict the top 5 facilities in terms of highly cited study publication numbers in May to June 2020 and November to December 2022, respectively. The top 5 institutional affiliations by highly cited studies per 2-month period in May to June 2020 were based in China (Huazhong University: 14.7 studies; University of Hong Kong: 6.8 studies; Wuhan University: 4.8 studies; Zhejiang University: 4.5 studies; Fudan University: 4.5 studies); however, by 2021, they all displayed a decreasing trend ( Figure 4 A). Conversely, in November to December 2022, the top 5 affiliations by highly cited studies per 2-month period were based in the US or the UK (Harvard University: 15.0 studies; University College London: 11.0 studies; University of Oxford: 10.2 studies; University of London: 9.9 studies; Imperial College London: 5.8 studies) ( Figure 4 B). Although there was some turnover, the trend remained the same in sensitivity analyses. There were more facilities in China in May to June 2020 by the full counting method (eFigure 4 in Supplement 1 ) and by affiliations of corresponding authors (eFigure 5 in Supplement 1 ) and more facilities in the US or UK in November to December 2022 by the full counting method (eFigure 4 in Supplement 1 ) and by affiliations of corresponding authors (eFigure 5 in Supplement 1 ).

Figure 5 provides an overview of the research fields of affiliations with the highest number of highly cited studies in May to June 2020 ( Figure 5 A) and November to December 2022 ( Figure 5 B). Huazhong University of Science and Technology published the greatest number of highly cited studies in May to June 2020, with 27 of 34 studies in the clinical medicine field. In contrast, the top highly cited studies in November to December 2022 were from Harvard University, with 73, 13, and 9 highly cited studies in the fields of clinical medicine, molecular biology and genetics, and psychiatry and psychology, respectively.

This cross-sectional study evaluated trends in COVID-19 research by analyzing highly cited studies every 2 months from January 2020 to December 2022. As the pandemic progressed, the number of highly cited studies related to COVID-19 increased sharply. Nevertheless, after reaching a peak at the end of 2021, the number of highly cited studies exhibited a declining trend. In addition, while most highly cited studies were initially from the field of clinical medicine, we observed an increase in the number of publications from other fields through the observational period. Over time, there was a shift in the ranking of countries, with the US overtaking China to produce the highest number of highly cited studies since September to October 2020. The number of highly cited studies from China showed a decreasing trend, while those from the UK exhibited an increasing trend. Institutions that published the greatest number of highly cited studies at the beginning of the pandemic were from China; however, their number of publications gradually decreased, and the top institutions were replaced by those from the US and UK.

To our knowledge, no studies to date have conducted a bibliometric analysis of highly cited studies related to COVID-19 over the past 3 years. However, a bibliometric analysis using the COVID-19 Open Research Dataset (CORD-19) 15 reported that COVID-19 studies, not just highly cited studies, published in 2020 came mostly from the US, China, and UK, which received more than 60% of citations. Similar to our research, that study found that the US steadily increased the number of studies and took the top spot, China had initially led COVID-19 research but experienced a substantial decline in research output over time, and the UK showed the opposite trend, starting with a slow pace of publications and gradually increasing its contributions throughout the year. Another study 16 examined the association of COVID-19–related publications with overall publication rates in high-impact factor journals. They showed that studies related to COVID-19 accounted for approximately 10% to 50% of the total number of publications in each high-impact journal from 2020 to 2021. Additionally, a gradual decline in COVID-19–related publications was observed at the end of 2020. Notably, this declining trend was detected earlier in that study than in our analysis, suggesting a lag in citations given that highly cited studies are determined based on the number of citations after publication.

As reported in a previous study, 17 an increase in COVID-19 cases in a region or country was associated with increased COVID-19–related research activity in that area. This may be associated with 2 factors: the need for data and increased government funding for research to control the pandemic. In addition, our findings of a gradual downward trend in highly cited studies related to COVID-19 may have been associated with a decrease in global attention to COVID-19 research. This trend may also suggest that researchers have gained a better understanding of the etiology and treatment of COVID-19, leading to decreased interest or fatigue with the topic. 18 Changes in distribution, top affiliations, and research fields of highly cited studies suggest a gradual shift in interest in COVID-19 research toward more diversified and broader research areas. Based on results of this investigation, we expect a sustained reduction in the number of highly cited studies on COVID-19. Furthermore, we speculate that the research focus may further diversify, and we intend to examine this hypothesis through ongoing analysis. A bibliometric analysis using highly cited studies may be an appropriate method to capture trends in research by examining high-profile studies every 2 months. Similar methods used in this study may be useful for analyzing research trends in other fields.

This study has several strengths. First, to our knowledge, it is the first bibliometric analysis of highly cited studies related to COVID-19. Investigations of highly cited studies (ie, those with the top 0.1% of citations) may be of greater interest than studies that analyze the total number of COVID-19–related studies. Using this method, we can exclude studies with low scientific impact, such as those published in predatory journals. In addition, highly cited studies are updated every 2 months, allowing the tracking of trends over time. Second, this study used fractional counting rather than full counting. While full counting is widely used in bibliometric analysis, fractional counting allows for field normalization and takes into account effects of aggregating large studies, particularly at the level of countries and research organizations. A comparative study 14 recommended fractional counting in such bibliometric studies.

This study also has several limitations. First, while fractional counting is a strength, it can also be a limitation given that full counting is more widely used, making it challenging to compare our results with those of other studies. However, we also performed full counting as sensitivity analyses and observed no substantial difference in trends compared with fractional counting. This suggests that the difference in counting methods may not be a serious issue. Second, highly cited studies are limited to the top 0.1% of citations and are not representative of all published literature. In addition, the number of citations does not necessarily guarantee the quality of the research. Therefore, it should be noted that this study’s findings represent only trends in influential research. Third, although we used ESI categories to define research fields, these categories do not classify highly cited studies in detail. For example, clinical medicine includes a very broad range of highly cited studies. A more detailed classification may be appropriate for a closer look at research trends. Fourth, although this study identified trends in COVID-19–related highly cited studies, it provides a broad overview rather than a detailed analysis. Several interesting aspects could not be explored in depth in this study, including comparisons with non-COVID-19–related highly cited studies and the evolution of characteristic topics over time, such as lockdown policies and vaccines. To address these gaps, we need further analyses in the future.

In this cross-sectional study, a bibliometric analysis of highly cited studies found that as the COVID-19 pandemic evolved over the 3 years since its outbreak, there was a shift in trends in COVID-19 research. The increase and decrease in the number of highly cited studies related to COVID-19 may suggest shifting interests of researchers. Meanwhile, there was a noticeable increase in the number of topics covered by field, including not only clinical medicine but also a diverse range of topics.

Accepted for Publication: July 31, 2023.

Published: September 8, 2023. doi:10.1001/jamanetworkopen.2023.32802

Correction: This article was corrected on October 16, 2023, to fix the name of an institution in the author affiliations.

Corresponding Author: Satoshi Funada, MD, PhD, Department of Health Promotion and Human Behavior, School of Public Health, Graduate School of Medicine, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan ( [email protected] ).

Author Contributions: Dr Yoshida had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Funada, Yoshida, Katanoda.

Acquisition, analysis, or interpretation of data: Funada, Yoshioka, Luo, Iwama, Mori, Yamada, Yoshida, Furukawa.

Drafting of the manuscript: Funada, Iwama, Katanoda.

Critical review of the manuscript for important intellectual content: Yoshioka, Luo, Mori, Yamada, Yoshida, Furukawa.

Statistical analysis: Funada, Yoshioka, Mori, Yamada, Yoshida.

Obtained funding: Katanoda.

Administrative, technical, or material support: Mori.

Supervision: Yoshida, Katanoda, Furukawa.

Conflict of Interest Disclosures: Dr Funada reported receiving grants from the Japan Society for the Promotion of Science (JSPS), KDDI Foundation, and Pfizer Health Research Foundation outside the submitted work. Dr Yoshioka reported receiving grants from the JSPS and Japan National Cancer Center outside the submitted work. Dr Luo reported receiving grants from the JSPS outside the submitted work. Dr Furukawa reported receiving personal fees from Boehringer-Ingelheim, DT Axis, Kyoto University Original, Shionogi, and Sony and grants from Shionogi outside the submitted work and having patents pending for 2020-548587, 2022-082495, and intellectual properties for Kokoro-app licensed to Mitsubishi-Tanabe. No other disclosures were reported.

Funding/Support: We acknowledged the support by grant JPMH21HA201 from the Japan Ministry of Health, Labour and Welfare Research Program on Emerging and Reemerging Infectious Diseases and the Japan National Institute of Public Health for language editing and article publishing charges.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We thank Editage for providing English language editing. This company was paid a fee for these services.

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v.99(43); 2020 Oct 23

The COVID-19 research landscape

Junhui wang.

a Institute of Medical Information, Chinese Academy of Medical Sciences

b Digital China Health Technologies Co. Ltd., Beijing, China.

Objectives:

The Coronavirus Disease 2019 (COVID-19) caused heavy burdens and brought tremendous challenges to global public health. This study aimed to investigate collaboration relationships, research topics, and research trends on COVID-19 using scientific literature.

COVID-19-related articles published from January 1 to July 1, 2020 were retrieved from PubMed database. A total of 27,370 articles were included. Excel 2010, Medical Text Indexer (MTI), VOSviewer, and D3.js were used to summarize bibliometric features.

The number of the COVID-19 research publications has been continuously increasing after its break. United States was the most productive and active country for COVID-19 research, with the largest number of publications and collaboration relationships. Huazhong University of Science and Technology from China was the most productive institute on the number of publications, and University of Toronto from Canada ranked as Top 1 institute for global research collaboration. Four key research topics were identified, of which the topic of epidemiology and public health interventions has gathered highest attentions. Topic of virus infection and immunity has been more focused during the early stage of COVID-19 outbreak compared with later stage. The topic popularity of clinical symptoms and diagnosis has been steady.

Conclusions:

Our topic analysis results revealed that the study of drug treatment was insufficient. To achieve critical breakthroughs of this research area, more interdisciplinary, multi-institutional, and global research collaborations are needed.

1. Introduction

A novel coronavirus emerged and caused a rapid spread of phenomena in Wuhan, China, at the end of 2019. In February 11, 2020, the World Health Organization named this disease Coronavirus Disease 2019 (COVID-19). [ 1 ] With the global spread of COVID-19, it threatened human lives, caused heavy burdens, and brought tremendous challenges to social development. To support the public health decision-making and scientific countermeasures implementation, researchers around the world were racing to study on the disease transmission, diagnostic tests, treatments, vaccines, among others. With the joint efforts of researchers and clinicians around the world, more and more COVID-19-related articles have been published and the outputs of scientific research are constantly emerging. As of July 1, 2020, PubMed has included 27,370 published articles on COVID-19.

State of the art literature review about COVID-19 demonstrated that most available literature-based studies could be basically divided into 2 kinds. The first kind is systematic reviews or meta-analyses. Most of them focused on a certain specific subfields of COVID-19 research, such as drug therapy, diagnostic methods, or clinical symptoms. For example, Alzghari et al [ 2 ] performed a systematic review to investigate the effect of Tocilizumab on COVID-19, and Zhu et al [ 3 ] systematically reviewed the CT imaging features of COVID-19 to provide reference for clinical practice. The second kind is the bibliometric analysis which uses quantitative analysis methods to describe literature in a particular research domain. However, some of the bibliometric analysis were targeting at coronavirus, not just severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), for the purpose of providing reference for COVID-19 research, and the time window was usually set for a long retrospective duration. [ 4 – 7 ] For example, Mao et al [ 7 ] analyzed coronavirus articles published from 2003 to 2020. Up to the investigation time of this study, there were limited number of bibliometric studies specific to COVID-19 and most of them were found and implemented at early stage of COVID-19 outbreak. [ 8 , 9 ] For example, Lou et al [ 8 ] executed a query in PubMed using keyword “COVID-19” and analyzed 183 related articles. Most of these previous literature-based studies of COVID-19 provided a specific review for COVID-19 research progresses or clinical observations; however, the description of a whole picture of COVID-19 scientific research using systematical methods was still insufficient.

Therefore, to answer who, what, where, and when questions of COVID-19 studies, we adopted a hybrid method that integrated multi-approaches, including bibliometrics, topic analysis, collaboration analysis, trends analysis, and visualization, to give a timely and systematic review of COVID-19 literatures. The analysis objectives include countries/regions, institutes, collaboration relationships, research topics, and research trends of COVID-19 studies.

2. Materials and methods

2.1. data source.

The data scope of this study is COVID-19-related articles published from January 1, to July 1, 2020. Since PubMed has served as the primary database for retrieving biomedical literature, it was selected as the only data source. [ 10 ] Ethical approval was not required because no human and animal subjects were enrolled.

2.2. Search strategy

The advanced search option was adopted, and the query “((novel coronavirus[Title/Abstract] OR COVID-19[Title/Abstract] OR 2019-nCov[Title/Abstract] OR SARS-Cov-2[Title/Abstract] OR COVID19[Title/Abstract] OR coronavirus disease 2019[Title/Abstract] OR coronavirus disease-19[Title/Abstract]) OR COVID-19[Supplementary Concept]) AND (“2020/01/01”: “2020/07/01”[dp])”was executed on July 1, 2020. In total, 27,370 COVID-19 articles were collected.

2.3. Data collection

All of the retrieved articles were downloaded and saved with PubMed default format. Microsoft Excel 2010 was used to pre-process the data and, in conjunction with Visual Basic for Applications (VBA), to extract analysis objects such as country/region names and institute names. The number of publications of a country is derived by counting the number of publications that contain at least one author's affiliation belongs to this country, and the first affiliation will be selected when an author has more than one affiliations.

2.4. Bibliometric and visualized analysis

MTI (National Library of Medicine, Bethesda, MD), [ 11 ] VOSviewer 1.6.15 (Leiden University, Leiden, Netherlands) [ 12 ] and D3.js (Mike Bostock, Observable, Inc., San Francisco, CA) [ 13 ] were used to carry out bibliometric and visual analysis of the publications. Since Medical Subject Headings (MeSH) represent much richer semantics that author-selected keywords, they were chosen as the object of topic analysis. MTI was used to extract MeSH terms from title and abstract of articles because newly created articles in PubMed will not be indexed with MeSH terms immediately. VOSviewer was used to generate collaborative network of countries/regions/institutes and co-occurrence network of MeSH terms. Finally, D3.js was used to visualize the internal hierarchy and the popularity trend of topics, which identified by MeSH terms co-occurrence clustering.

2.5. Analytical methods

Topic popularity was calculated by proportional frequency equation and tracked in a certain period of time window (10 days window) to identify the research trends. The equation of proportional frequency is as follows:

Where Dpro_t is the proportional frequency of the term in the t time window, D_t is the document frequency of the term, that is, the number of publications containing the term. DAll_t is the total number of publications and DAvg is the average number of publications on each time window. Topic popularity is measured by adding up proportional frequency of all the terms in this topic.

3.1. The Scale of COVID-19 publications

The number of COVID-19 research publications has been continuously increasing after its break. According to the growth trend from the view of global to country level, as shown in Figure Figure1, 1 , United States overtook China Mainland as the largest contributor in publishing COVID-19-related articles in early May 2020. As of July 1, 2020, United States had published 5949 (21.7% of the total) articles, and China Mainland had published 4080 (14.9% of the total) articles in total that are much higher than any of the other countries. The following Italy (10.7%) and UK (8.4%) were also prolific among the top 10 countries (Table (Table1). 1 ). In addition, China Mainland had the highest rate of domestic collaboration (79.4%), whereas Australia had the lowest (34.8%) among the top 10 productive countries.

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The growth trend on number of publications about COVID-19 research.

The top 10 productive countries/regions that published COVID-19 research.

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3.2. The collaborative network of countries/regions

Collaboration activities on country/region level were measured based on co-author analysis. As shown in Figure Figure2, 2 , there were 76 countries/regions involved in COVID-19 research collaboration which divided into 3 clusters.

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The collaboration network on COVID-19 research across countries/regions.

Cluster 1 (blue color) mainly included United States, China Mainland, Canada, and Australia, which were all ranked as Top 10 productive countries. When measuring the collaboration activities, our study further disclosed that United States and China Mainland played the leading role of the COVID-19 research. These two countries had strong internal co-authorship relations, and at the same time had strong external co-authorship relations with other countries/regions. Cluster 2 (green color) was composed with 27 European countries that included UK, Italy, Germany, and France, among others. There were frequent internal collaboration activities among these European countries. In addition, Cluster 3 (red color) included India, Brazil, and other countries of Asia, Africa, and South America with a relatively low frequency of internal collaboration.

Furthermore, total link strength analysis showed that United States was the most active country with the highest number of collaboration relationships with other countries/regions. United States and China Mainland had the largest number of link strength compared with other countries, with a total of 439 collaboration papers. However, Chinese researchers had mostly co-authored with their domestic collaborators, only 20.6% of the studies were collaborated with international researchers outside China Mainland (Table (Table1 1 ).

3.3. The collaborative network of research institutes

The most productive institutes were located at United States, China Mainland, and Europe. There were 307 institutes that had published >10 articles. Table Table2 2 lists the number of publications and internal collaboration publications for top 10 productive institutes. Huazhong University of Science and Technology (523), Wuhan University (340), and University of California (300) were ranked as Top 3 productive institutes by number of publications. Besides, the BMJ editors published 193 latest news and comments about COVID-19 research with the highest rate of internal collaboration of 100%.

The top 10 productive institutes that published COVID-19 research.

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Collaboration network among productive institutes was generated based on co-author analysis. Institutes were clearly separated into 5 clusters as shown in Figure Figure3. 3 . Cluster 1 (red color) included 96 institutes which were mostly universities and hospitals of United States, as well as 10 universities from Canada, among which University of Toronto ranked as Top 1 institute for global research collaboration with the largest number of total link strength. Besides, University of California and University of Washington were also the collaboration centers with large number of co-authored articles. The universities, hospitals, and research institutes came from China composed Cluster 2 (blue color), from which Huazhong University of Science and Technology and Wuhan University had the largest number of link strength compared with other institutes, with a total of 60 collaboration papers. Furthermore, >100 institutes from Europe composed Cluster 3 (green color) and Cluster 4 (yellow color), of which universities and hospitals from Italy composed Cluster 4 and the remaining institutes composed Cluster 3. According to co-author analysis on these 2 clusters, University College London and University of Oxford were most active on research collaboration with other institutes. In addition, it was interesting to observe that Cluster 5 (purple color) contributed a relatively small volume of publications but was a self-centered research community mainly composed with 8 universities from Iran.

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The collaboration network on COVID-19 research across institutes.

3.4. The identified COVID-19 research topics

To achieve better understanding of what are the researcher's focuses and research progress of COVID-19 with its break timeline, MeSH terms of each article were selected as the observation objects to measure the research topics and topic trends. On the analysis of selected 2000 MeSH terms with their frequency above 10, a MeSH terms co-occurrence network with 584 high-frequency terms were generated, as shown in Figure Figure4. 4 . The network center nodes are COVID-19, severe acute respiratory syndrome coronavirus 2, and Coronavirus Infections. Four topics about COVID-19 research were obviously identified: epidemiology and public health interventions, virus infection and immunity, clinical symptoms and diagnosis, drug treatments, and clinical studies, as shown in Figure Figure5 5 .

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The MeSH terms co-occurrence network on COVID-19 research.

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The hierarchy of four identified COVID-19 topics.

3.4.1. Topic I: epidemiology and public health interventions

The research topic of epidemiology and public health interventions had gathered great attentions. It contained 281 of the 584 MeSH terms, indicating that the prevention and control of COVID-19 was the most concerned issue at all the stages of disease break. It mainly contained epidemic transmission dynamics, prevention and control measures and effect analysis at different regional levels (global, national, and urban), [ 14 , 15 ] epidemiological investigation, modeling, and trend prediction from the perspective of public health, [ 16 , 17 ] as well as various personal protective measures (Disinfection, Hand Hygiene, Masks, Personal Protective Equipment, Protective Devices), [ 18 , 19 ] and social prevention and control measures (Airway Management, Mass Screening, Social Distance, Social Isolation). [ 20 ] In addition, high attention had been paid to the psychological and mental state (Anxiety, Anxiety Disorders, Depression, Fear, Mental Disorders, Mental Health) of the general public, infected people, and medical workers. [ 21 ]

3.4.2. Topic II: virus infection and immunity

A total of 168 MeSH terms were included in this topic, which was mainly for the molecular biology and immunology studies of SARS-CoV-2 for the purpose of detection and prevention. Three subtopics of Topic II were identified based on content analysis. The first subtopic was the research on the pathogenesis of COVID-19 that included the replication process and infection mechanism of SARS-CoV-2 in human cells, with emphasis on the interaction between SARS-CoV-2 and biological enzymes (RNA-directed DNA polymerase, angiotensin-converting enzyme [ACE2], serine endopeptidases). [ 22 , 23 ] The second subtopic was the studies on the etiological detection methods of SARS-CoV-2 and the most important methods involved were real-time polymerase chain reaction and reverse transcriptase polymerase chain reaction (PCR). [ 24 , 25 ] In addition, COVID-19 vaccine development with the aim of inducing immune response composed the third subtopic. [ 26 , 27 ]

3.4.3. Topic III: clinical symptoms and diagnosis

A total of 111 MeSH terms were included in Topic III, which mainly covered clinical symptoms of COVID-19 patients and various testing methods used for diagnosis. The clinical symptoms (or complications) of COVID-19 mentioned in the literature mainly included: abdominal pain, cough, diarrhea, dyspnea, fatigue, fever, headache, leukopenia, lymphopenia, myalgia, nausea, pharyngitis, pleural effusion, pneumonia, pulmonary embolism, respiratory distress syndrome, respiratory insufficiency, vomiting, among others. [ 28 , 29 ] The diagnostic methods, mostly discussed in the literature, were routine blood tests (alanine transaminase, aspartate aminotransferases, biomarkers, C-reactive protein, leukocyte count, l -lactate dehydrogenase, lymphocyte count, neutrophils, platelet count) and imaging examinations (radiography, tomography, x-rays). [ 30 ]

3.4.4. Topic IV: drug treatments and clinical studies

Topic IV contained 24 MeSH terms, which was the smallest topic. The research content in this topic was mainly in vivo and in vitro trials of multiple drugs and their combinations for the purpose of treating COVID-19. The studied drugs involved antibacterial/antiviral drugs (azithromycin, favipiravir, lopinavir, remdesivir, ribavirin, ritonavir), antimalarials, and rheumatoid arthritis drugs (chloroquine, hydroxychloroquine, tocilizumab) among others. Because of the difference of clinical endpoint and experimental design, the trials results obtained so far are not consistent. For example, some researchers conclude that remdesivir can be used as potent drugs against COVID-19 [ 31 ] ; however, some studies show that remdesivir cannot significantly improve the symptoms of patients with severe COVID-19. [ 32 ] Chloroquine and hydroxychloroquine are in a similar situation to remdesivir. [ 33 , 34 ] Therefore, there is still no widely accepted standard on specific drugs or the best drug treatment options of COVID-19. [ 35 – 37 ]

3.5. Topic popularities and evolvements about COVID-19 research

Topic popularity of the above 4 COVID-19 topics was measured by using proportional frequency equation in Section 2, and the measured results, as shown in Figure Figure6, 6 , were consistent with manually validation results by reviewing literature. According to trend analysis, the topic of epidemiology and public health interventions has gathered great attentions and continuously with high popularity. The characteristics of SARS-CoV-2, such as biological structure, genetic sequence, and infection mechanism, have been well studied, and beyond this, consensus has been reached on COVID-19 clinical symptoms and diagnostic methods.

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Trends of topic popularity.

On the topic tracking analysis of epidemiology and public health interventions, we found that most of the early studies and reports were mainly focus on China's epidemic prevention and control. [ 38 , 39 ] By implementing a series of preventive control and medical treatment measures, the pandemic in China had been effectively contained, but the number of confirmed cases outside China continued to increase, as did the corresponding research on epidemiology and public health interventions, which was consistent with the continuously high popularity trending curve of this topic (blue curve), as displayed in Figure Figure6 6 .

For virus infection and immunity study, the topic popularity decreased since early of February 2020. As studying the etiological characteristics of a novel virus, such as biological structure, genetic sequence, and infection mechanism, is the key to pandemic prevention and control, the trend curve of Topic II was in the highest position in the pre-outbreak period (January 2020). With the joint efforts of scientists around the world, substantial progress had been achieved in the understanding of SARS-CoV-2. For example, the genetic sequencing of SARS-CoV-2 was performed by Chinese scientists on January 7, 2020 and the results were timely shared with the WHO on January 12, 2020. Furthermore, the infection mechanism of SARS-CoV-2, especially its relationship with ACE2 was identified, and specific diagnostic PCR tests were produced. [ 40 , 41 ] The above achievements were mainly completed in January and February 2020, starting from February, the trend curve of Topic II gradually declined. However, the curve will remain at a high level because more and more attentions have been paid to vaccine-related research. According to literature reports, there are more than 100 candidate vaccine projects targeting COVID-19 worldwide, and some of them have entered clinical trials. [ 42 , 43 ]

With the continuous increase of confirmed and treated cases, clinicians achieved deeper understanding about COVID-19. Since March 2020, there has been a global consensus on the symptoms and diagnostic criteria for COVID-19. [ 28 , 44 ] In addition, the seventh and final edition of “Diagnosis and Treatment Protocol of COVID-19,” issued by the National Health Commission of the PRC, was also released on March 3, 2020. [ 45 ] As a result, the trend curve of Topic III starts to smooth out since March 2020 (Fig. (Fig.6 6 ).

Although lopinavir/ritonavir was recommended as antiviral drug by the first edition of “Diagnosis and Treatment Protocol of COVID-19” on January 16, 2020 at the beginning of the pandemic, the widespread interest in using antiviral drugs to treat COVID-19 began with a report of the first diagnosed patient who benefit from remdesivir in United States, which was published in NEJM on January 31, 2020. [ 46 ] Therefore, the trend curve of Topic IV in Figure Figure6 6 has risen slightly since February 2020. However, the minimal topic size and low trend curve suggest that drug therapy remains the weak point in the response to COVID-19.

4. Discussion and conclusion

The number of COVID-19 publications has been growing dramatically since March 2020. According to our search strategy, as of the submission of this manuscript (July 13, 2020), the number of COVID-19 publications has exceeded 30,000. Given that COVID-19 pandemic has not been well contained at the global level, relevant research will continue to be carried out and the number of publications will increase accordingly. The methodology in this study can be easily implemented to analyze the future research status of COVID-19, or even applied to other fields.

Although United States and China were the most productive countries, they were not in the identical situation. Since the initial outbreak was in China, Chinese scholars quickly carried out a series of studies and published numerous articles in the early stages of the epidemic. However, Chinese scholars tend to collaborate with domestic scholars rather than aboard. Unlike China, United States has seen a significant increase in the number of publications since April 2020, and has quickly occupied the highest level of participation in global collaboration due to its strong scientific research strength and influence.

Collaboration at the institutional level has obvious geographical characteristics, especially the frequent internal collaborations among institutes located in China, as well as United States. For example, Huazhong University of Science and Technology and Wuhan University, which ranked first and second by the number of publications, co-authored a total of 60 articles, making up the most productive institute pair. Both universities are located in Wuhan and their affiliated hospitals, such as Tongji Hospital, Union Hospital, and Renmin Hospital, are major hospitals for treating COVID-19 patients. The front-line clinical medical workers in those hospitals have conducted a lot of research on virus detection, clinical diagnosis and treatment while fighting against the epidemic.

COVID-19 research topics are continuously evolving with their publication timeline, measuring these changes will help researchers and scientific policy makers understanding the status of COVID-19 research. As indicated by the trend curves of topic popularity, the prevention and control of COVID-19 remains the most important issue at present, and drug therapy remains the weak point in the response to COVID-19. In addition, more support should be given to vaccine research and development, because vaccines are the ultimate solution to the epidemic. [ 5 ]

This study provided an overall investigation of COVID-19 scientific progresses using multiple qualitative and quantitative analysis methods. The collaboration status of COVID-19 research at national and institutional levels was disclosed and 4 topics (epidemiology and public health interventions, virus infection and immunity, clinical symptoms and diagnosis, drug treatments, and clinical studies) were identified and interpreted. Our topic analysis results revealed that the study of drug treatment was insufficient. To achieve critical breakthroughs of this research area, more interdisciplinary, multi-institutional, and global research collaborations are needed.

4.1. Strengths and limitations

Publications on COVID-19 research were retrieved from PubMed, and the collaboration status and research trends of COVID-19 were measured via bibliometric and visualized analysis, which was considered to be relatively objective and comprehensive. Moreover, well curated MeSH terms were used as the object of topic analysis in this study, compared with author-selected keywords which were usually chosen by existing COVID-19-related bibliometric analysis. [ 4 – 7 ] Due to the limited number and randomness of author-selected keywords, the derived results, especially the co-occurrence analysis results, cannot reflect the real status of the COVID-19 research. Our MeSH terms-based methodology could better disclose the research topics and trends of COVID-19. However, limitations also exist in our research. On the one hand, PubMed was selected as the only data source, so some articles only indexed in other databases such as Web of Science and Scopus might be left out. On the other hand, for the sparisity reason of citation network of published COVID-19 articles, citation analysis has not been adopted in this study. In the future, studies based on citation analysis, such as identification of influential authors and highly-cited articles, will be conducted and included in our further analysis.

Author contributions

Conceptualization, N.H.; Data curation, J.W.; Software, J.W. and N.H.; Visualization, J.W. and N.H.; Writing—original draft, J.W. and N.H.; Writing—review & editing, J.W. and N.H. All authors have read and agreed to the published version of the manuscript.

Conceptualization: Na Hong.

Data curation: Junhui Wang.

Software: Junhui Wang, Na Hong.

Visualization: Junhui Wang, Na Hong.

Writing – original draft: Junhui Wang, Na Hong.

Writing – review & editing: Junhui Wang, Na Hong.

Abbreviations: ACE2 = Angiotensin Converting Enzyme 2, COVID-19 = Coronavirus Disease 2019, MeSH = Medical Subject Headings, MTI = Medical Text Indexer, SARS-COV-2 = severe acute respiratory syndrome coronavirus 2, VBA = Visual Basic for Applications.

How to cite this article: Wang J, Hong N. The COVID-19 research landscape: Measuring topics and collaborations using scientific literature. Medicine . 2020;99:43(e22849).

The authors report no conflicts of interest.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

COVID-19 Research

Stanford Medicine scientists have launched dozens of research projects as part of the global response to COVID-19. Some aim to prevent, diagnose and treat the disease; others aim to understand how it spreads and how people’s immune systems respond to it.

Below is a curated selection, including summaries, of the projects.

To participate in research , browse COVID-19 studies . Our research registry also connects people like you with teams conducting research to make advances in health care. If you are eligible for a study, researchers may contact you to provide additional details on how to participate.

By participating in clinical research, you help accelerate medical science by providing valuable insights into potential treatments and methons of prevention.

Stanford COVID-19 Study Directory Stanford Medicine Research Registry

To improve our ability to determine who has COVID-19 and treat those infected.

Transmission

To better prevent and understand the transmission of the coronavirus.

Vaccination and Treatment

To improve our ability to prevent COVID-19 and treat those infected.

Epidemiology

To better understand how the coronavirus is spreading.

Data Science and Modeling

To better predict medical, fiscal and resource-related outcomes of the COVID-19 pandemic.

To better understand immune responses to the coronavirus.

Cardiovascular

To better understand the way the virus affects the cardiovascular system.

To better enable the workforce to achieve its goals during the COVID-19 pandemic.

Miscellaneous

A variety of other research projects related to the COVID-19 pandemic.

The list isn’t comprehensive and instead represents a portion of Stanford Medicine research on COVID-19. If you are a Stanford Medicine scientist and would like to see your research included here, please send a note to: [email protected].

The Stanford Institute for Human-Centered Artificial Intelligence has also created a webpage for COVID-19 research collaborations and other opportunities, such as research positions, internships and funding. If you would like to submit an opening please use the following form and they will post it on their website.

Support Stanford Medicine’s response to COVID-19 by making a gift .

COVID-19 Research Projects

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

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Peer-reviewed

Research Article

One-year in: COVID-19 research at the international level in CORD-19 data

Roles Conceptualization, Data curation, Formal analysis, Project administration, Writing – original draft

* E-mail: [email protected]

Affiliation John Glenn College of Public Affairs, The Ohio State University, Columbus, Ohio, United States of America

Roles Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

Affiliation School of Public Affairs, Zhejiang University, Hangzhou, Zhejiang, China

Roles Conceptualization, Formal analysis, Software, Validation, Visualization

Affiliation Australian Artificial Intelligence Institute, University of Technology Sydney, Ultimo, Australia

Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Affiliation Shidler College of Business, University of Hawaiʻi at Mānoa, Honolulu, Hawaiʻi, United States of America

Caroline S. Wagner,
Xiaojing Cai,
Yi Zhang,
Caroline V. Fry

Published: May 25, 2022
https://doi.org/10.1371/journal.pone.0261624
Peer Review
Reader Comments

The appearance of a novel coronavirus in late 2019 radically changed the community of researchers working on coronaviruses since the 2002 SARS epidemic. In 2020, coronavirus-related publications grew by 20 times over the previous two years, with 130,000 more researchers publishing on related topics. The United States, the United Kingdom and China led dozens of nations working on coronavirus prior to the pandemic, but leadership consolidated among these three nations in 2020, which collectively accounted for 50% of all papers, garnering well more than 60% of citations. China took an early lead on COVID-19 research, but dropped rapidly in production and international participation through the year. Europe showed an opposite pattern, beginning slowly in publications but growing in contributions during the year. The share of internationally collaborative publications dropped from pre-pandemic rates; single-authored publications grew. For all nations, including China, the number of publications about COVID track closely with the outbreak of COVID-19 cases. Lower-income nations participate very little in COVID-19 research in 2020. Topic maps of internationally collaborative work show the rise of patient care and public health clusters—two topics that were largely absent from coronavirus research in the two years prior to 2020. Findings are consistent with global science as a self-organizing system operating on a reputation-based dynamic.

Citation: Wagner CS, Cai X, Zhang Y, Fry CV (2022) One-year in: COVID-19 research at the international level in CORD-19 data. PLoS ONE 17(5): e0261624. https://doi.org/10.1371/journal.pone.0261624

Editor: Alberto Baccini, University of Siena, Italy, ITALY

Received: July 14, 2021; Accepted: December 6, 2021; Published: May 25, 2022

Copyright: © 2022 Wagner et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: https://doi.org/10.6084/m9.figshare.16620274.v1 .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The COVID-19 pandemic upended many normal practices around the conduct of research and development (R&D); the extent of disruption is revealed across measures of scientific research output [ 1 – 3 ]. This paper revisits the extent to which patterns of international collaboration in coronavirus research during the COVID-19 pandemic depart from ‘normal’ times. We present publication patterns using one full year of publications data from the CORD-19 database, and observations on non-COVID peer-reviewed publications using the Web of Science, to examine national and international publication rates and network patterns. We examine topics of research on COVID-19, and reflect on lessons learned about international collaboration from the disruption. The analysis may be useful to research administrators, international affairs professionals and science studies scholars.

We study the international collaborative linkages as a network. In the absence of a global governing body, international collaborations operate by network dynamics. Scientific connections at the global level reflect collective decisions of hundreds of individuals who seek to connect to each other. Connections are not random; they are influenced by five factors: two personal and three contextual. Personal choices tend towards those previously known or known by reputation or introduction. Contextual choices are 1) resources available, 2) geopolitical factors, and 3) time and attention. Network dynamics emerge from interplay of these factors, although there is little research on how a disaster, such as a pandemic, will affect productivity, collaboration, and topic focus. Moreover, it is difficult to determine expectations of network dynamics in a pandemic because global exogenous disruptions are rare and studies about science in a disaster are sparse. This paper seeks to fill some of these gaps.

This paper is organized to describe the literature supporting our inquiries, and to present hypotheses derived from the literature. We then describe coronavirus research prior to the pandemic, and early policy responses to the crisis. A section on data and methodology presents approaches designed to answer the questions emerging from the hypotheses. A results section describes outcomes of the analyses, followed by limitations of the data and approaches presented here. A discussion section details responses to the hypotheses as well as observations about the research project and avenues for further research. An S1 Appendix provides additional technical details.

Literature review and hypotheses

In the decades preceding the pandemic, R&D spending and output grew rapidly. OECD data shows that, among member nations, R&D spending was 25% higher in 2017 than a decade earlier. The US National Science Foundation (NSF) reports that from 2008–2018 the annual number of citable publications (articles, notes and letters, hereafter, “publications”) worldwide grew by 3.83% per year from 1.8 million to 2.6 million [ 4 ]. Increases in spending, trained practitioners, and publications contributed to an overall growth of the research enterprise in natural sciences and engineering, social sciences, and arts and humanities. Within the research enterprise, among scientifically advanced nations, international collaborative publications grew at a faster rate than national publications, accounting for as much as one-quarter of all publications in 2018, with variations observed across fields, according to the National Science Foundation [ 4 ]. Those fields that rely on large-scale equipment are more highly globalized, but increases in international linkages is observed in most fields, tied, not to funding or equipment, but to the interests of researchers to work together. The size of these teams has grown larger over time [ 5 ].

International collaborative patterns have been dominated by scientifically advanced nations, although, over time, many low-income, emerging and developing nations have become more active, and have partnered with more advanced nations [ 6 ]. Some tendency to collaborate among nations with former colonial ties is observed [ 7 ], but this is likely due to incentivized funding provided by the former colonial power. Political differences do not appear to hinder collaboration, evidenced most notably by the rise of China to be the number one collaborating nation with the United States. Abramo et al. [ 8 ] added to literature on tendency of neighbors to work together, but Choi [ 6 ] shows this tendency to be decreasing over time.

Prior research into collaboration around viral disease events found that, during the 2014 West African Ebola epidemic, collaboration grew between scientists from scientifically advanced nations and the most affected nations [ 9 ], suggesting that connections were made based upon disease location. Ebola outbreaks brought in researchers from scientifically advanced nations to work with local researchers on specific events. Collaborative ties did not persist past the disease event.

A global community of coronavirus researchers predated the advent of the 2019 novel coronavirus; this community formed after the 2002 SARS coronavirus epidemic [ 1 ]. As the new threat emerged in 2019, governments provided emergency R&D funding to encourage targeted research on the novel coronavirus. Most of these funds were committed by governments in scientifically advanced countries and were allocated to national institutions, although the European Union (EU) and the US National Institutes of Health (NIH) fund both national and foreign applicants. National actors receiving funds may then choose, in some instances, to connect to foreign collaborators, creating an international connection. The resulting connections can be studied through coauthorship attributions on paper and interpreted as a self-organizing network of connectivity [ 10 ]. In Fry et al. [ 1 ], we showed that, during the early months of the COVID-19 pandemic, international collaborations in coronavirus research emerged among just a few nations, and, on average, publications had fewer coauthors per paper than pre-pandemic levels. Most nations did not publish on the novel coronavirus in early pandemic research.

We expect that, as funds became available to researchers, and as more knowledge was generated through the first year of the pandemic, cross-national collaborative ties will grow. That said, because of travel limitations and a need for urgent results, we expect the rate of international collaboration and network ties to remain lower than pre-pandemic levels. This expectation is also informed by the research of Rotolo and Frickel [ 11 ] who found that there were fewer ties and smaller teams among researchers just after a hurricane disaster. Further, based upon findings in the wake of the Fukushima disaster [ 12 ] and a survey by Myers et al. [ 2 ] we expect that attention to pandemic-related R&D (including basic science, patient care, and public health) has lessened the output of other scientific research as well as reduced the rate of international collaborations in other fields. In addition to changed collaborative patterns, we expect to see changes in topics throughout the first year of the pandemic with topics becoming more focused as knowledge about events grows, which we explore in a separate article. In Zhang et al. [ 3 ], we showed that, at the beginning of the pandemic, the disrupted knowledge system exhibited very little topic focus. As the pandemic progresses, we expect to see greater topic focus. We further expect to see international collaboration focus on basic science and less on patient care and public health which may have a local, regional, or national characteristics. We expect continued consolidation among leading nations and elite institutions through the pandemic year due to pressures for rapid results and the lack of mobility to begin new collaborations. Further, we expect that geographic distance will mean less during the pandemic because remote collaborators will rely on communications technologies rather than face-to-face consultations.

Science during the COVID-19 pandemic

Coronavirus research predated the COVID-19 crisis, but it was a community of 22,000 researchers working on SARs, MERs, and the porcine diarrhea epidemic [ 1 , 3 ]. Coronavirus research output doubled in number over the decade between 2008–2018, in keeping with numbers in the biological sciences. As the new threat of a novel coronavirus emerged in 2019, governments provided emergency R&D funding to encourage targeted research, which attracted many new researchers from a wide range of fields. More than 156,000 researchers published on COVID-19 in 2020, growing the original community that had worked on coronaviruses by over 130,000.

The United States Government committed the largest amount of funds to the novel coronavirus, through the CARES Act and other legislation, allocating at least $5 billion to basic research, applied research, and development of vaccines, diagnostics, mapping of disease occurrence, analytics, public health, and medicine. The bulk of funds were appropriated by the US Congress to the U.S. National Institutes of Health (NIH), and through them to BARDA, the Biomedical Advanced Research and Development Authority. Other agencies also received additional R&D funds over and above their annual appropriations, including the National Science Foundation ($74 million) and the Department of Energy ($99.5 million). The U.S. government also provided funds to private companies to aid in vaccine development and procurement. For example, Moderna, a pharmaceutical company headquartered in Massachusetts, received $1 billion of R&D funds and in $1.5 billion in advanced purchase agreements.

Germany provided $891 million in R&D funds into coronavirus as well as to vaccine development. The European Commission provided €469 million in R&D funds, along with permission to recipients to reallocate funds originally slotted for other topics. The UK government reports spending £554 million on 3,600 initiatives related to COVID-19. In China, the Ministry of Science and Technology invested $100 million for emergency projects and unknown millions of funds for vaccine development, and the National Natural Science Foundation of China also reallocated approximately $15 million for projects related to COVID-19.

Many research organizations and researchers from various disciplines shifted to focus on aspects of the pandemic and received grant funds to do so. Just as with any other R&D funding, the expectation is that funded research will result in published works, enhanced equipment, and medicines and vaccines. Very early in the pandemic, preprints [ 13 ] (non-peer-reviewed articles) and peer reviewed articles began flooding into publishing venues. The number of scholarly publications related to the crisis grew spectacularly in the early months of the pandemic [ 1 ].

The rush to publish is expected: Zhang et al. [ 14 ] note that historical patterns show that researchers have, in previous cases, responded quickly to public health emergencies with publications, which is the same pattern we see with COVID-19 research. In updating our earlier work [ 15 ], we found that the number of coronavirus publications in CORD-19 grew considerably in the early days of the novel coronavirus, rising at a spectacular rate from a total of 4,875 articles produced on the topic (preprint and peer reviewed) between January and mid-April to an overall sum of 44,013 by mid-July, and accumulated to 87,515 by the start of October 2020. (In comparison, nanoscale science was a rapidly growing field in the 1990s, but it took more than 19 years to go from 4,000 to 90,000 articles [ 16 ]).

The dissemination of publications changed during the pandemic. COVID-19 peer-reviewed and edited publications became available to other researchers through new (CORD-19) and pre-existing (National Library of Medicine) web platforms. COVID-19 publications were much more likely than other works to be published as open access in 2020 [ 17 ]. In 2020, open-access, peer-reviewed publications related to COVID-19 accounted for 76.6% of all publications compared to 51% of all non-COVID publications. Highly cited papers—those in the top 1% most highly cited, with over 500 citations—were more likely than other work to be published in subscription-based journals such as The Lancet , Science , New England Journal of Medicine or Nature but these works were placed into open Web portals for rapid access. The National Library of Medicine served as a repository for most new publications related to COVID-19. The publishing house Elsevier—which publishes many subscription-based journals—created a "Public Health Emergency Collection" to make COVID-19 articles rapidly available regardless of the access status of the original work (subscription or open access). Similarly, CORD-19 (the database which provided data for this article) through Semantic Scholar, made relevant research (including historical work) rapidly and readily available and allowed researchers to deposit work they viewed as relevant.

Researchers from China and the USA increased the rate of collaborative publications on coronavirus in the earliest days of the pandemic Fry et al. [ 1 ]. Liu et al. [ 18 ] showed a surge of what they call ‘parachuting collaborations’–new connections not seen prior to the pandemic–which dramatically increased during the pandemic. Together with the findings in Fry et al. [ 1 ], these findings suggests that search and team formation changed to adapt to the needs of COVID-19 research, a finding also reported by Lee & Haupt [ 19 ]. Liu et al. [ 18 ] found that COVID-19 research papers were less likely to involve international collaboration than non-COVID-19 papers during the same time period, a finding reported by Aviv-Reuven & Rosenfeld [ 20 ] as well, a finding we can confirm.

Several research articles note the absence of emerging and developing nations in early COVID-19 research. Fry et al. [ 1 ] and Lee & Haupt [ 19 ] showed that very few developing nations were involved in the earliest day of the crisis. Zhang et al. [ 3 ] confirmed Fry et al. in finding that the USA, China, and the UK were the three countries with the largest number of articles by mid-year. Several articles report that fewer coauthors appear on article bylines [ 1 , 20]. This is likely due to the need for rapidity in responding to the crisis: fewer coauthors reduces the time needed to communicate, synthesize and submit results.

Data and methodology

Data for this study were extracted in March 2021 from the Covid-19 Open Research Dataset, “CORD-19,” an open resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems for COVID-19 research over its rich collection of metadata and structured full-text papers. It is accessible through the National Library of Medicine, National Institutes of Health, USA. In addition, we accessed the whole of Scopus 2020 data to examine non-COVID publications over the year. To search for evidence of government funding for COVID-19 research, we searched Web of Science, which has a field for funding acknowledgements. (Non-COVID publications were any peer-reviewed, published work that did not include one of the keywords for the COVID search below).

To maintain consistency across our studies, we applied the same search terms as used in Fry et al. [ 1 ], Cai et al. [ 15 ], and Zhang et al. [ 3 ] and limited the search to the dates January 2020 to December 2020 and citation data to March 2021. The following search terms were applied to titles and abstracts to obtain an initial dataset of coronavirus publications:

coronavirus
corona virus
Severe Acute Respiratory Syndrome
Middle East Respiratory Syndrome

The initial dataset was cleaned to remove the following artifacts: conference papers, preprints, collections of abstracts, symposia results, articles pre-dating 2020, and meeting notes. Preprints were excluded in this report to avoid double-counting in cases where a work is subsequently peer-reviewed and published. The author names, institutional affiliation, and addresses were extracted for analysis. For articles derived from the PubMed Central website, the citation count was extracted up to March 2021. The resulting dataset provided us with 106,993 publications for the calendar year 2020. The final dataset was further divided into four quarters, shown in Table 1 , according to “Published Date”, i.e., the electronic publication dates (if any) or else print publication date: January to March (2020 Q1), April to June (2020 Q2), July to September (2020 Q3), and October to December (2020 Q4). Full counting is used to count the number of publications of a specific country or institution. Among all the publications with at least one author and address, 8,158 (8.9%) are single-author articles, with the rest involving coauthors at the national (78%) or international levels, with 20,203 (22.0%).

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We analyzed the number of authors and coauthors per paper for descriptive statistics of people publishing on the novel coronavirus; we analyzed keyword usage and topics drawn from keywords and abstracts, and we analyzed geographic location of authors to study cooperative patterns at the international level. We collected additional data to answer questions about activities not available in CORD-19 in funding and on non-COVID research publications. We compared the CORD-19 data to a defined dataset of coronavirus research derived from scientific articles on coronavirus-related research on historical data we had earlier extracted from PubMed, Elsevier’s Scopus, and Web of Science (details of the construction of this data can be found in Fry et al. [ 1 ]). The datasets are available at https://figshare.com/articles/dataset/One_Year_of_COVID-19_Int_l_Collaboration/16620274 . For the CORD-19 articles that are also indexed in Clarivate’s Web of Science (WoS), we retrieved funding information to get a rough view about which funding agency is contributing to the coronavirus research in the pandemic period; 35% of CORD-19 articles acknowledged funding. Dimensions database was used to analyze the open access categories.

To test for change in the number of participants in research groups between pre-COVID-19 and COVID-19 periods, we use double-tailed T-tests to compare the average “team” structure between periods. (We employ the word “team” for convenience to describe coauthor groups even though we do not know the mechanism of cooperation among the group.) Statistical significance is assessed at 0.05 level. Team structure is measured as average number of authors per publication, average number of nations per publication, and the percentage share of internationally collaborated articles. We also use regression models to test the relationship between team structure and citation impact. Since the dependent variable, i.e., citations, is a non-negative integer, we apply count-data regression models (i.e., negative binomial regression) that can account for the nature of the data.

To test the relationship between geographic distance and international collaboration among nations, we calculate the geographic distance and collaboration strength between country pairs. The geographic distance between nations is defined as distance between capitals of each nation, based on geographic data about world cities in the R package “map”. Following the normalization approach used in previous research [ 6 , 24 , 25 ], we apply Salton’s cosine measure of international collaboration strength, which takes the publication size of nations into account. It is calculated as the number of collaborative publications divided by the square root of the product of the number of publications of the two collaborating nations (See Appendix Table 2 in S1 Appendix ).

The process of collecting and cleaning the CORD-19 database produced a set of 106,993 publications. The set used for this study is limited to work published in 2020, responding to the search string. We compared the CORD-19 results to Elsevier’s Scopus for 2020: the search of Scopus produced 73,000 COVID-19 publications, so fewer than CORD-19. Scopus limits its indexing of publications to specific journals, while CORD-19 encouraged open deposit of materials, which would include venues not indexed by Scopus—this likely accounts for the differences in numbers among databases.

For all research in 2020, Scopus shows a total of 2,584,701 publications, COVID and non-COVID topics (recall that non-COVID topics are not included in CORD-19). Against expectations, growth in life and health sciences output between 2019 and 2020 is shown in most disciplines of life and health sciences fields, both COVID and other topics. The number of publications on novel coronavirus and resulting disease is about 20 times higher in 2020 than coronavirus research published between 2018 and 2019, when work focused on SARS and MERS—earlier disease events that were not as devastating as COVID-19.

Fig 1 shows the number of coronavirus publications by month compared to the number of reported disease case outbreaks worldwide. Publication numbers grew quickly between February and May 2020 at the same time as the number of COVID-19 cases increased at an alarming rate. Since May 2020, the number of publications has remained stable at over 10,000 publications per month, while the rate of growth in COVID-19 cases declined slightly relative to the earliest months. The surge of publications on COVID-19 clearly result from thousands of ‘new’ researchers from various fields publishing on coronavirus in 2020. In pre-COVID-19 period, coronavirus researchers were drawn mainly from Life Sciences & Biomedical Sciences (e.g., Virology, Infectious Disease) and Natural Sciences (e.g., Multidisciplinary Chemistry and Organic Chemistry). The pandemic calls upon researchers from all research fields, with noticeable increased efforts from Social Sciences (including the authors of this work).

Data on publications and cases are collected from CORD-19 and WHO ( https://covid19.who.int/ ).

https://doi.org/10.1371/journal.pone.0261624.g001

Table 2 shows the top 25 life sciences fields in 2020 from Scopus, with the total number of COVID-19 and non-COVID articles in the same field. Fields that show the highest number of COVID-19 research are medicine, infectious disease, and public health. For all research in life and health sciences, highest growth is seen in surgery, plant science, and psychiatry and mental health. We can assume that the COVID-19 articles were written in 2020, since they are topical—“COVID” was not a keyword in 2019. Moreover, journal editors greatly sped up the processing time for COVID-related review and publication [ 26 , 27 ]. Conversely, the non-COVID articles may represent work conducted years prior, since it takes time to write, review and publish research results [ 28 ]. In fact, peer review in non-COVID related disciplines was delayed in 2020 due to the pandemic [ 26 ] so there may be insufficient time to fully assess the impact of the crisis on non-COVID research of publication output. Data in 2021 will be more telling of the impact of the pandemic year on non-COVID research publication patterns.

Data: Elsevier’s Scopus.

https://doi.org/10.1371/journal.pone.0261624.t002

Contributions by author location

COVID-19 publishing numbers differ considerably by regions of the world. Fig 2 shows regionally aggregated contributions on COVID-19. Asian countries contributed over one-third of world publications in early 2020, but this percentage share dropped in the later months of 2020 as China reduced its output. Europe showed the opposite trend: Europe’s share of world COVID publications increased since April 2020 and the number remained stable through the latter months of 2020. North America’s share of publications increased throughout 2020.

https://doi.org/10.1371/journal.pone.0261624.g002

As expected, scientifically advanced nations, including China, account for the majority of COVID-19 publications. Among all nations publishing related work, USA, China, and UK produced (together and separately) 50% of the coronavirus articles during 2020, shown in Table 3 . As of early 2021, their publications accumulated around 68% of citations made to global publications supporting the expectation of consolidation around expertise and reputation. In the earliest days of the pandemic, three articles from Chinese authors [ 29 – 31 ] contributed key findings that guided much of the ensuing research; each of these articles garnered thousands of citations. Italy, UK, India, and Spain were slower to begin publishing but became more prolific through 2020, and particularly so in the final quarter. Fig 3 shows the rapid growth of monthly publications for selected countries, which also tracks with the trend in national COVID-19 cases; this finding is similar to one found in the 2014 West African Ebola epidemic [ 9 ].

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https://doi.org/10.1371/journal.pone.0261624.t003

International collaborative publication rates in coronavirus research took six months to recover to pre-COVID levels. Collaborative research projects take longer to publish results, so the ‘recovery’ time may simply reflect more communication and production time needed due to the physical distances and time zone differences. As expected, during 2020, international collaborative papers showed fewer nations per paper in the early days, but this number increased through the year. This number stabilizes in the fourth quarter to pre-pandemic levels. Supporting other findings [ 18 ], about 65% of internationally coauthored papers include only two nations—this is a drop from usual patterns.

In earlier work, we noted that developing nations were largely absent from the publication records in the early COVID-19 period. We explored the participation of developing nations in global coronavirus research over the full year, expecting to see some recovery, but it was weak. Pre-COVID-19 coronavirus research in 2018–2019 shows that low-income nations [ 32 ] accounted for 26% of all nations participating in the research, publishing 4% of global articles. This drops during 2020: In the first two quarters of 2020, low-income nations accounted for 21% of active nations and produced 3.4% of global articles (Low-income countries (LIS) are defined by the World Bank, https://data.worldbank.org/country/XM . China, India and Brazil are not low-income countries.). That said, throughout 2020 low-income nations increase their contribution to the coronavirus research, contributing just slightly more in number of publications compared to their participation in pre-COVID-19 period, but much lower than scientifically advanced nations. Against expectations and in contrast to the trend before the pandemic and in the first few months of 2020, we find, by mid-year, Chinese institutions no longer appear in the list of top 10 producing institutions, supporting Liu et al. [ 18 ]. For example, the University of Hong Kong and the Chinese Academy of Agricultural Sciences ranked third and fourth in pre-COVID-19 research but dropped down the list in 2020. This drop tracks with the drop in number of COVID-19 cases in China.

Academic institutions worldwide were responsible for the largest share of publications about coronavirus during the pandemic, although private companies participated in research, usually through coauthorship with academic coauthors. We identified a list of 40,287 institutions involved in coronavirus research with the following rules: (1) we retrieved valid institution names with a list of key strings, such as “hospital”, “univers*”, and “instit*”; and (2) we consolidated variations of the same institutions, such as “MIT” and “Massachusetts Institute of Technology”, and “University of Sydney” and “Sydney University”. Fig 4 shows the institutions making top contributions to COVID-19 cooperation. As expected, the figure shows that highly reputed institutions—Harvard University (Massachusetts, USA), Huazhong University of Science and Technology (Wuhan, China), and the University of California System—produced the largest absolute numbers of publications on coronavirus in 2020. From the CORD-19 data, we find there are 2,232 articles (2.43%) involving authors from private corporations, which is average for corporate participation in Web of Science [ 33 ]. Nevertheless, this percentage is a drop from private sector participation in pre-COVID-19 dataset, where we found that 3.4% of articles involved the private sector, so it was higher than average and dropped to average in 2020.

The percentage shares of publications in global articles of top 10 prolific institutions in COVID-19 (2020) and pre-COVID-19 (2018–2019) are shown.

https://doi.org/10.1371/journal.pone.0261624.g004

Table 4 shows the most frequently acknowledged funding agencies in coronavirus research indexed in Web of Science in 2020. Funders from the USA, China, and UK (or Europe) are the most commonly acknowledged, which is consistent with the publication outputs as shown in Table 3 . The US National Institutes of Health (NIH), the largest scientific organization dedicated to health and medical research, tops the list and is acknowledged in 15.8% of funded articles. The dominant funder in China, National Natural Science Foundation of China (NSFC), ranks second and contributes to 10.4% of publications, despite decreased publication shares in later periods. European funding agencies, including European Commission, UK Research & Innovation (UKRI), and Medical Research Council UK (MRC), also play a vital role as COVID-19 cases and number of publications increase in Europe.

https://doi.org/10.1371/journal.pone.0261624.t004

Co-occurrence network on coronavirus research

Terms retrieved from titles and abstracts of research articles provide useable clues to understand topic focus (Data for network analysis is available at Wagner, Caroline (2021): Figshare_Network files.rar. figshare. Dataset. https://doi.org/10.6084/m9.figshare.16652752.v1 ). The co-occurrence between terms and entities (e.g., funding agencies), and among terms, reveals their semantic connections in research, and may answer questions such as which agencies support which topics. The connection is measured by how many times two terms appear in proximity in the entire dataset, within and across articles. We identified 4,865 terms from a raw set of 1.2 million terms retrieved from titles and abstracts of the 106,993 articles published in 2020 via natural language processing techniques and a term clumping process [ 34 ], with the aid of VantagePoint (VantagePoint is a text mining tool for analyzing bibliometric data. See the link: https://www.thevantagepoint.com/ ). Specifically, the term clumping process facilitated a set of thesauri to remove meaningless terms (e.g., conjunctions, pronouns, and prepositions) and common terms in academic articles (e.g., “method” and “conclusion”), and it then consolidated terms with the same stem (e.g., terms in singular and plural forms).

Fig 5 analyzes the co-occurrence between the top 20 high-frequency terms and the major funding sources, visualized by Circos [ 35 ]. The Chinese agency, National Science Foundation of China (NSFC), was much more likely to fund research related to “Wuhan” while the United States’ National Institutes of Health (NIH) is much more likely to fund research with the term “United States.” Small differences can be observed for “clinical characteristics” (proportionately more from NSFC), and “hospitalization” (proportionately more from NIH) but these two terms are quite similar, so differences may be due to semantics only. Aside from these two differences, it appears that each of the agencies fund similar term portfolios differentiated only in proportion to their contribution, so more focused on basic research, which was our expectation.

https://doi.org/10.1371/journal.pone.0261624.g005

To assess whether international collaborations had different topic focus from domestic collaborations—which we expected—we analyzed co-terms at both levels. Fig 6 shows the co-term network for the internationally collaborative research, and Fig 7 shows the co-term network for domestic-only collaborations. In both networks, one sees the topics that are shown in Fig 5 as focus areas for government funders. With data extracted using Vantagepoint’s Natural Language Processing function ported into VOSViewer, Fig 6 shows international collaboration dominated by three clusters: 1) research on the virus (red, bottom left), 2) on patient care (purple, top right), and 3) on public health (green, bottom left).

Interactive version accessible at https://app.vosviewer.com/?json=https://drive.google.com/uc?id=1MVWE1bsTGi6jJeeU7BNCcjKTd2yjTiv0 .

https://doi.org/10.1371/journal.pone.0261624.g006

Interactive version accessible at https://app.vosviewer.com/?json=https://drive.google.com/uc?id=1RraBpIYbLY5_DfOMC0YJ7IZoq3_sRiKa .

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Fig 7 shows domestic topics, highlighting greater emphasis on patient care and disease characteristics (gold, top center) than seen in Fig 6 . Moreover, a fourth cluster emerges (blue, center) with details about outbreaks, effects, viral loads, and other aspects of health are seen that are not as prevalent at the international level. A table in the S1 Appendix provides more details about the topics. As expected, the domestic publications focus on public health and patient care more than is seen at the international level, where basic science dominates the topics.

Collaboration rate

Table 5 compares collaboration rates in coronavirus publications before the COVID-19 pandemic and in four quarters of 2020. As expected, and in comparison to the number of co-authors in pre-COVID-19 coronavirus research, publications in 2020 show fewer authors, fewer nations per paper, and less frequent international collaboration overall. Team size—represented by the number of authors on a publication—shrank shortly after the outbreak of COVID-19, a finding we highlighted in Fry et al. [ 1 ], but by the end of the year, the number had recovered and risen to just above levels seen in pre-COVID-19 coronavirus research. As expected, the average number of nations per international publication remained at lower levels than pre-COVID-19 levels for USA articles; there was no significant difference in numbers of international partners for Chinese articles in pre-COVID-19 and COVID-19 periods.

https://doi.org/10.1371/journal.pone.0261624.t005

We explored the relationship between team structure as represented by coauthors on papers and the number of citations to COVID-19 publications for publications produced in 2020 (looking at citation records up until March 2021). Table 5 shows the regression results that explore the relationship between citations to publications and international collaborative team structure, for publications with authors from USA, China, and the UK respectively. As expected, a positive correlation is shown between numbers of citations and international collaboration. Further, also meeting expectations, there is a correlation between number of authors and citations, which may reflect an audience affect due to a larger reader network. Also as expected, international teams attracted more citations than domestic-only teams, again, with a possible audience affect. These findings held for all nations except the USA, where international articles are not cited more than domestic-only research when holding constant the number of authors ( Table 6 , column 6).

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Network analysis

Figs 8 and 9 compare internationally collaborative networks in the pre-COVID-19 (2018–2019) and COVID-19 (2020) periods. Recall that these numbers represent about 22% of all COVID research in 2020. Fig 8 shows pre-COVID coronavirus research with two large clusters: one, a European cluster, and two, a global cluster brokered by the USA. The US collaborated closely with China, the UK, France, the Netherlands, and Germany. The dominance of the USA is partly accounted for by the volume of research when compared to the output per nation; the USA leads in publications, citations, and connectivity in coronavirus research prior to the pandemic and remained the leader during the pandemic.

Interactive version accessible at https://app.vosviewer.com/?json=https://drive.google.com/uc?id=1_1ASbt-FheQE6_VQNc5eFhy578jot9co .

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Interactive version accessible at https://app.vosviewer.com/?json=https://drive.google.com/uc?id=1TLdcW-lNUQ1k0kDlpZYOUQL57E8MKNqM .

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Fig 9 shows the COVID-19 collaborative network, where, as expected based upon research by Rotolo & Frickel [ 11 ], we observe more clusters and more brokering hubs than pre-COVID-19. The number of clusters has grown, with four clusters revealing a broader set of countries acting as centralized nodes or hubs, with the UK, Italy, and Germany increasing their bridging role from positions shown in the pre-COVID-19 network. The European research clusters form into two large groups, one with Italy as the brokering hub and one with the UK in a central brokering hub. Italy intensively links to France, the USA, and Switzerland. African nations join the network through the UK connection. Australia is central to a cluster that includes Spain, Brazil, and many smaller nations from South America. As expected, geographic distance between country pairs is negatively related to the collaboration strength of the two countries in all cases (see Appendix Table 2 in S1 Appendix ). However, during COVID-19 period, the negative impact of geographic distance on collaboration strength was weakened as demonstrated by positive and significant coefficient of interaction of COVID and geographic distance. As expected, physical distance was less of a barrier to collaboration than in other scientific research. The result reveals that the pandemic weakened the role of geographic distance in international collaboration (OLS regression analysis of the relationship between geographic distance and collaboration strength).

Table 7 shows the network metrics for the above networks and for four quarters of 2020. (Visuals of the four quarterly networks are shown in the S1 Appendix ) Consistent with the growing number of internationally collaborative articles throughout 2020, the network statistics reveal expanded connections from the first period (January to March) to the third period, cumulative (July to September) in 2020, but then stabilizing.

https://doi.org/10.1371/journal.pone.0261624.t007

The network statistics suggest that COVID-19 research involved many more participants than those who worked on coronavirus in the years prior to the pandemic, as we find by examining the number of unique author names. From the pre-COVID-19 coronavirus network to the COVID-19 network, we see that number of nodes increases from 103 nations before COVID to 173 during COVID. More importantly, the number of links among nations more than triples, suggesting that many more connections were made at the international level than existed prior to the pandemic. Average degree doubles, supporting the observations of many new links. These links likely were forged remotely in a process that Liu et al. [ 18 ] call “parachuting collaborations” that post-date the pandemic. These types of collaborations may have emerged through friend-of-friend connections, since people could not meet face-to-face due to travel restrictions during 2020. Betweenness centrality drops over the year indicating a shift in influence of the initial, dominant hubs to include more participants from more nations over the pandemic year.

Table 8 shows the network metrics for top producers (USA, UK, and China) in the global network. As shown by the average degree of the three countries, the USA was a hub in the network in both periods, more so early in the pandemic, supporting our expectation of consolidation around expertise and reputation, but betweenness centrality drops as researchers from more countries joined into the research. The UK played a much more active role in the COVID network compared to China in the second half of the year. Despite being a hub in the pre-COVID network and in the first months of the pandemic [ 1 ], China played a less prominent role in the network in 2020.

https://doi.org/10.1371/journal.pone.0261624.t008

Limitations of this research

This research project had a number of limitations of data, time, analysis, and scope. Data limitations include constraints of what is measurable in published work (publications, networks, and citations). We decided to use CORD-19 data because it was the most expansive dataset for COVID-19 research, but we may have picked up lower quality work as a result; it is an open dataset with attendant problems. We extracted desired features, but there may be gaps and errors. In order to get a count of open-access publications, we used Dimensions data, but the total number of COVID-19 publications in Dimensions were lower than CORD-19, so open-access publications are likely under-counted. We present the percentages of COVID-19 research that is open access; these calculations are broadly representative, but they cannot be further verified. Moreover, we are unable to show extent of R&D occurring in private research laboratories if it is not published. We hoped to inform policymakers about COVID-19 research trends in a timely manner, which meant we worked with data available at the time (in Spring 2021) rather than waiting until data has been expanded, cleaned or validated. Elsevier and Clarivate databases were also examined; these databases are more carefully curated for quality. We tapped them for comparisons to CORD-19, and especially for non-COVID research. We had planned to test whether the pandemic had an impact on non-COVID research but we were unable to show this outcome: During 2020, peer reviews were delayed [ 36 ], researchers were not able to access labs or other resources, and scholarship was interrupted, but these obstacles are not yet evident in the data. Disruptions to 2020 research activities likely will not be seen in publication data until 2022 and after. We also exclude preprint publications from this analysis, which could present a limitation, given the important role of preprints during the pandemic.

Further, a limitation of this analysis is the reliance on nations as ‘super-nodes’ in a network that consists of individuals with associated cultural contexts that are not captured in network data. This reliance on nations is partly justified in that nations represent the underlying political and social systems that support scientific activities by offering funding, infrastructure, training, and dissemination of results. We acknowledge that the reliance on nations as a unit of measurement is a limitation, imposed on the analyst based upon the ways in which data are collected. Future research will need to ensure cleaner data to validate comparisons presented here. None of these datasets can truly represent the scope of activities that contributed to what is known about COVID-19, and mechanisms to assess knowledge flows are quite limited and time consuming to collect.

A review of one year of research publications about the novel coronavirus that emerged in Wuhan China in late 2019 shows the research community reacting rapidly and robustly to the challenge. The rapidity of response to COVID-19 suggests flexibility in the research system: thousands of researchers from many fields began working on the crisis. Research was disseminated initially in a flood of preprints; rapidly peer-reviewed publications were placed on open data platforms or shared openly on subscription-based platforms. COVID-related, peer-reviewed publications rose sharply in number in early 2020, and these publications were much more likely to be shared on open-access platforms or formats to enable rapid knowledge diffusion than is the norm in scholarly publishing [ 17 ]. The earlist COVID-19 research efforts were conducted by China, the USA, and the UK, and these three nations constitute close to 50% of all COVID publications on the subject in 2020. European nations started off slowly in research publications, but these nations continued to grow their output throughout 2020, as China’s output dropped.

As expected, international collaborations accounted for a smaller percentage of publications than is generally seen in ‘normal’ times, where internationally co-authored articles often account for more than one-quarter of articles [put new footnote here that was added on page 4 and delete this note] We expected the drop-off in international publications because a lack of mobility meant that people were unable to meet and discuss shared insights, or to devise, carry out or compare research results. Remote collaborations involve higher transaction costs and could be expected to slow progress. This possibly explains why international teams were smaller: to cut down on the time needed for communication. Further, the lower rate of international collaboration may be due to topics related to patient care and public health specific to particular regions or nations rendering them less suited to international collaboration [ 3 ]. We also noted that distance was less of a barrier to collaboration than is shown in other studies in times before the COVID-19 crisis.

Interestingly, the number of papers per nation tracks closely with the outbreak of COVID-19 cases in that nation. We expected to find numbers of publications to be more closely correlated to research funding. This may still be the case, but the data is too variable and incomparable to elicit a correlation between funding and output. We surmise that researchers were motived by a desire to be helpful to those suffering with the disease. It may also be the case that local COVID cases provided observational opportunities for researchers, and thus publication opportunities, as well, which produced data that resulted in more national publications.

The low rate of participation by lower-income nations was somewhat unexpected. Lower-income nations had a very low rate of participation in the early days of COVID, and only slowly joined the global publication counts. This may be due to a number of factors, including the need to publish locally to address the crisis, the inability of researchers to access laboratories or data during lock-down periods, the lack of access to one’s office, or the inability of national ministries to provide emergency research funds. People may not have had access to the Internet at home. The lower showing for developing nations is a concern, since these nations need the scientific knowledge to battle viral events just as much or more than advanced nations. This finding clearly requires more research and perhaps policy action.

We expected that the combined rush to work on COVID and the pandemic lock-downs would reduce non-COVID research activities. This may still be the case (reported in a survey by Myer et al. [ 2 ]), but it could not be detected in publication numbers at this writing. Publications in life and health sciences in non-COVID topics increased in number over 2019. As researchers become more comfortable with remote work, they may have persisted in publishing earlier results, however, this does not comport with the findings of Myer et al. [ 2 ]. As the pipeline catches up these activities, it will be worth revisiting the impact on productivity again at the end of 2021.

The stratification and consolidation comport with a model of global science as a reputation-based system creating a social hierarchy: the global network reverts to scientifically advanced nations and elite institutions in a crisis. We expected that the number of papers would align with reputation and resources. The role of reputation is confirmed by the consolidation of actors to fewer, elite institutions in scientifically advanced nations cooperating together more so than prior to the pandemic. The role played by access to resources is unclear—we observe that national output is closely tied to number of COVID-19, which could be due to a desire of scientists to help the effort. This requires more inquiry.

The influence of geopolitical factors also appears to play some role in research output, partnership and productivity. Arguably, Chinese publications initiated most of the COVID-19 research into the nature of the SARS-CoV-2 virus itself [ 29 – 31 ]. Nevertheless, in April 2020, the Chinese government changed requirements for review of articles related to the origins of COVID-19, requiring a more central review for work about the source of the novel coronavirus, which may have reduced the willingness of Chinese authors to cooperate internationally, although this requires more inquiry. Negative political comments made in the United States against China regarding the source of the virus may also have dampened international collaboration, although we did not test for this possibility.

Time and attention may have played a role in the drop in the share of internationally co-authored papers. Transaction costs of distance communications may have hampered some international connections. The drop-off in number of developing nations participating at the start of the pandemic may have contributed to the drop in international collaboration numbers, as well, by lowering the number of potential collaborators.

A remaining question arises around the mechanisms by which people, who had not already worked together before the pandemic, became connected to one another in a year when most people were physically isolated from each other. These connections are what Liu et al. [ 18 ] term “parachuting collaborations.” One would expect that people connect face-to-face: Research shows that the vast majority of collaborative projects start face-to-face or side-by-side. When that cannot take place, it is unclear whether people look for physically proximate partners, choose to work alone, become connected to new people through friend-of-a-friend, through social media, or perhaps just a ‘cold-call’ outreach to someone they do not know. It is clear that many of the connections made around COVID-19 may not have existed prior to the pandemic, so further research is needed to understand how people connected with each other under crisis conditions.

Supporting information

S1 appendix. network visuals..

https://doi.org/10.1371/journal.pone.0261624.s001

Acknowledgments

Thanks go to Clayton E. Tillman and Thomas Collins for help with data collection and formatting.

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4. National Science Board. The state of U.S. science and engineering. Washington, DC: US Government Printing Office; 2020.
7. Glänzel W, Schubert A. Analysing scientific networks through co-authorship. In Handbook of quantitative science and technology research: The Use of Publication and Patent Statistics in Studies of S&T Systems, Moed H., ed., Kluwer Academic Publishers, Amsterdam, 2004. pp. 257–76.
13. Preprints contain the results of research activities, but they have not yet been subject to peer review.
17. Fry CV, MacGarvie M. Drinking from the firehose: Preprints, Chinese researchers, and the diffusion of knowledge in COVID-19. 2021.
18. Liu M, Bu Y, Chen C, Xu J, Li D, Leng Y, et al. Can pandemics transform scientific novelty? Evidence from COVID-19. arXiv. 2020.
20. Aviv-Reuven S, Rosenfeld A. Publication patterns’ changes due to the COVID-19 pandemic: A longitudinal and short-term scientometric analysis. arXiv. 2021:02594.
26. Horbach SPJM. No time for that now! Qualitative changes in manuscript peer review during the Covid-19 pandemic. Research Evaluation. 2021;(rvaa037).
32. DFID. Low-income countries (LIS) as defined by Department for International Development (DFID) of UK [cited 2021]. https://g2lm-lic.iza.org/call-phase-iv/list-of-lic/ .

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COVID-19 Research Articles Downloadable Database

March 19, 2020

Updated January 12, 2024

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Important announcement:

The CDC Database of COVID-19 Research Articles became a collaboration with the WHO to create the WHO COVID-19 database during the pandemic to make it easier for results to be searched, downloaded, and used by researchers worldwide.

The last version of the CDC COVID-19 database was archived and remain available on this website. Please note that it has stopped updating as of October 9, 2020 and all new articles were integrated into the WHO COVID-19 database . The WHO Covid-19 Research Database was a resource created in response to the Public Health Emergency of International Concern (PHEIC). Its content remains searchable and spans the time period March 2020 to June 2023. Since June 2023, manual updates to the database have been discontinued.

If you have any questions, concerns, or problems accessing the WHO COVID-19 Database please email the CDC Library for assistance.

Below are options to download the archive of COVID-19 research articles. You can search the database of citations by author, keyword (in title, author, abstract, subject headings fields), journal, or abstract when available. DOI, PMID, and URL links are included when available.

This database was last updated on October 9, 2020 .

The CDC Database of COVID-19 Research Articles is now a part of the WHO COVID-19 database . Our new search results are now being sent to the WHO COVID-19 Database to make it easier for them to be searched, downloaded, and used by researchers worldwide. The WHO Covid-19 Research Database was a resource created in response to the Public Health Emergency of International Concern (PHEIC). Its content remains searchable and spans the time period March 2020 to June 2023. Since June 2023, manual updates to the database have been discontinued.
To help inform CDC’s COVID-19 Response, as well as to help CDC staff stay up to date on the latest COVID-19 research, the Response’s Office of the Chief Medical Officer has collaborated with the CDC Office of Library Science to create a series called COVID-19 Science Update . This series, the first of its kind for a CDC emergency response, provides brief summaries of new COVID-19-related studies on many topics, including epidemiology, clinical treatment and management, laboratory science, and modeling. As of December 18, 2021, CDC has stopped production of the weekly COVID-19 Science Update.

Excel download:

Articles from August until October 9 2020 [XLS – 29 MB]
Articles from December 2019 through July 2020 [XLS – 45 MB]
The CDC Database of COVID-19 Research Articles is now a part of the WHO COVID-19 database . Our new search results are now being sent to the WHO COVID-19 Database to make it easier for them to be searched, downloaded, and used by researchers worldwide.
October 8 in Excel [XLS – 1 MB]
October 7 in Excel [XLS – 1 MB]
October 6 in Excel [XLS – 1 MB]
Note the main Excel file can also be sorted by date added.

Citation Management Software (EndNote, Mendeley, Zotero, Refman, etc.) download:

Part 1 [ZIP – 38 MB]
Part 2 [ZIP – 43 MB]
October 8 in citation management software format [RIS – 2 MB]
October 7 in citation management software format [RIS – 2 MB]
October 6 in citation management software format [RIS – 2 MB]
Note the main RIS file can also be sorted by date added.

The COVID-19 pandemic is a rapidly changing situation. Some of the research included above is preliminary. Materials listed in this database are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings.

To access the full text, click on the DOI, PMID, or URL links. While most publishers are making their COVID-19 content Open Access, some articles are accessible only to those with a CDC user id and password. Find a library near you that may be able to help you get access to articles by clicking the following links: https://www.worldcat.org/libraries OR https://www.usa.gov/libraries .

CDC users can use EndNote’s Find Full Text feature to attach the full text PDFs within their EndNote Library. CDC users, please email Martha Knuth for an EndNote file of all citations. Once you have your EndNote file downloaded, to get the full-text of journal articles listed in the search results you can do the following steps:

First, try using EndNote’s “Find Full-Text” feature to attach full-text articles to your EndNote Library.
Next, check for full-text availability, via the E-Journals list, at: http://sfxhosted.exlibrisgroup.com/cdc/az .
If you can’t find full-text online, you can request articles via DocExpress, at: https://docexpress.cdc.gov/illiad/

The following databases were searched from Dec. 2019-Oct. 9 2020 for articles related to COVID-19: Medline (Ovid and PubMed), PubMed Central, Embase, CAB Abstracts, Global Health, PsycInfo, Cochrane Library, Scopus, Academic Search Complete, Africa Wide Information, CINAHL, ProQuest Central, SciFinder, the Virtual Health Library, and LitCovid. Selected grey literature sources were searched as well, including the WHO COVID-19 website, CDC COVID-19 website, Eurosurveillance, China CDC Weekly, Homeland Security Digital Library, ClinicalTrials.gov, bioRxiv (preprints), medRxiv (preprints), chemRxiv (preprints), and SSRN (preprints).

Detailed search strings with synonyms used for COVID-19 are below.

Detailed search strategy for gathering COVID-19 articles, updated October 9, 2020 [PDF – 135 KB]

Note on preprints: Preprints have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information.

Materials listed in these guides are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings. HHS, PHS, and CDC assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by HHS, PHS, and CDC. Opinion, findings, and conclusions expressed by the original authors of items included in these materials, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of HHS, PHS, or CDC. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by HHS, PHS, or CDC.

To receive the COVID-19 Science Update, please enter your email address to subscribe today.

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How americans view the coronavirus, covid-19 vaccines amid declining levels of concern.

Just 20% of the public views the coronavirus as a major threat to the health of the U.S. population and only 10% are very concerned about getting a serious case themselves. In addition, a relatively small share of U.S. adults (28%) say they’ve received an updated COVID-19 vaccine since last fall.

How the Pandemic Has Affected Attendance at U.S. Religious Services

During the pandemic, a stable share of U.S. adults have been participating in religious services in some way – either virtually or in person – but in-person attendance is slightly lower than it was before COVID-19. Among Americans surveyed across several years, the vast majority described their attendance habits in roughly the same way in both 2019 and 2022.

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Here’s a look at what surveys by Pew Research Center and other organizations have found about Americans’ mental health during the pandemic.

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Online Religious Services Appeal to Many Americans, but Going in Person Remains More Popular

About a quarter of U.S. adults regularly watch religious services online or on TV, and most of them are highly satisfied with the experience. About two-in-ten Americans (21%) use apps or websites to help with reading scripture.

About a third of U.S. workers who can work from home now do so all the time

About a third of workers with jobs that can be done remotely are working from home all the time, according to a new Pew Research Center survey.

Economy Remains the Public’s Top Policy Priority; COVID-19 Concerns Decline Again

Americans now see reducing the budget deficit as a higher priority for the president and Congress to address than in recent years. But strengthening the economy continues to be the public’s top policy priority.

At least four-in-ten U.S. adults have faced high levels of psychological distress during COVID-19 pandemic

58% of those ages 18 to 29 have experienced high levels of psychological distress at least once between March 2020 and September 2022.

Key findings about COVID-19 restrictions that affected religious groups around the world in 2020

Our study analyzes 198 countries and territories and is based on policies and events in 2020, the most recent year for which data is available.

How COVID-19 Restrictions Affected Religious Groups Around the World in 2020

Nearly a quarter of countries used force to prevent religious gatherings during the pandemic; other government restrictions and social hostilities related to religion remained fairly stable.

What Makes Someone a Good Member of Society?

Most in advanced economies say voting, taking steps to reduce climate change and getting a COVID-19 vaccine are ways to be a good member of society; fewer say this about attending religious services.

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Coronavirus Disease (COVID-19): The Impact and Role of Mass Media During the Pandemic

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The outbreak of coronavirus disease 2019 (COVID-19) has created a global health crisis that has had a deep impact on the way we perceive our world and our everyday lives. Not only the rate of contagion and patterns of transmission threatens our sense of agency, but the safety measures put in place to contain ...

Keywords : COVID-19, coronavirus disease, mass media, health communication, prevention, intervention, social behavioral changes

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There is a current outbreak of Coronavirus (COVID-19) disease Find out more →

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Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus.

Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment. However, some will become seriously ill and require medical attention. Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. Anyone can get sick with COVID-19 and become seriously ill or die at any age.

The best way to prevent and slow down transmission is to be well informed about the disease and how the virus spreads. Protect yourself and others from infection by staying at least 1 metre apart from others, wearing a properly fitted mask, and washing your hands or using an alcohol-based rub frequently. Get vaccinated when it’s your turn and follow local guidance.

The virus can spread from an infected person’s mouth or nose in small liquid particles when they cough, sneeze, speak, sing or breathe. These particles range from larger respiratory droplets to smaller aerosols. It is important to practice respiratory etiquette, for example by coughing into a flexed elbow, and to stay home and self-isolate until you recover if you feel unwell.

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To prevent infection and to slow transmission of COVID-19, do the following:

Get vaccinated when a vaccine is available to you.
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Wear a properly fitted mask when physical distancing is not possible or when in poorly ventilated settings.
Choose open, well-ventilated spaces over closed ones. Open a window if indoors.
Wash your hands regularly with soap and water or clean them with alcohol-based hand rub.
Cover your mouth and nose when coughing or sneezing.
If you feel unwell, stay home and self-isolate until you recover.

COVID-19 affects different people in different ways. Most infected people will develop mild to moderate illness and recover without hospitalization.

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Seek immediate medical attention if you have serious symptoms. Always call before visiting your doctor or health facility.

People with mild symptoms who are otherwise healthy should manage their symptoms at home.

On average it takes 5–6 days from when someone is infected with the virus for symptoms to show, however it can take up to 14 days.

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Antiviral therapy for COVID-19 virus: A narrative review and bibliometric analysis

Affiliations.

1 School of Pharmacy, Management & Science University (MSU), Section 13, 40100 Shah Alam, Selangor, Malaysia. Electronic address: [email protected].
2 Department of Pharmacology, Global College of Pharmacy, Jawaharlal Technology University, Hyderabad (Jntuh) 501504, India.
3 Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia.
4 Department of Pharmacology and Toxicology, Pharmacy College, Tikrit University, Tikrit, Iraq.
PMID: 39244809
DOI: 10.1016/j.ajem.2024.09.001

The COVID-19 epidemic has become a major international health emergency. Millions of people have died as a result of this phenomenon since it began. Has there been any successful pharmacological treatment for COVID-19 since the initial report on the virus? How many searches are undertaken to address the impact of the infection? What is the number of drugs that have undergone investigation? What are the mechanisms of action and adverse effects associated with the investigated pharmaceuticals used to treat COVID-19? Has the Food and Drug Administration (FDA) approved any medication to treat COVID-19? To date, our understanding is based on a restricted corpus of published investigations into the treatment of COVID-19. It is important to note that no single study comprehensively encompasses all pharmacological interventions for COVID-19. This paper provides an introductory summary of a bibliometric analysis conducted on the data about COVID-19, sourced explicitly from two platforms, namely PubMed and ScienceDirect. The analysis encompasses the period spanning from 2019 to 2022. Furthermore, this study examines the published literature about the pharmacological interventions for the novel coronavirus disease 2019 (COVID-19), explicitly focusing on the safety and effectiveness of different medications such as Remdesivir (marketed as Veklury®), Lopinavir/Ritonavir (commercially known as Kaletra® or Aluvia®), Ribavirin, Favipiravir (marketed as Avigan®), Ivermectin, Casirivimab and Imdevimab (branded as Ronapreve®), Sotrovimab (marketed as Xevudy®), Anakinra, Molnupiravir, Nirmatrelvir/Ritonavir (marketed as Paxlovid®), and Galidesivir. Findings indicate that while Remdesivir and Nirmatrelvir/Ritonavir show significant efficacy in reducing hospitalization and severe outcomes, drugs like Lopinavir/Ritonavir and Ivermectin have inconsistent results. Our insights suggest a multifaceted approach incorporating these therapies can significantly improve patient outcomes. Repurposing drugs has been critical in rapidly responding to COVID-19, allowing existing medications to be used in new ways to combat the virus. Combination therapies and further research are essential to optimize treatment strategies.

Keywords: COVID-19; Coronavirus; Pharmacology; SARS-CoV-2; Treatment.

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Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Black Children
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Child Care and Early Education Research during the COVID-19 Pandemic: Lessons Learned and Future Considerations

Zipi Diamond
Sara Bernstein
Elizabeth Cavadel
Kyle DeMeo Cook
Stacy Ehrlich
Margaret Gillis
Hailey Heinz
Annalee Kelly
Gretchen Kirby
Michelle Maier
Heather Sandstrom
Kathryn Tout

The COVID-19 pandemic impacted all aspects of child care and early education (CCEE). Beginning in March 2020, the COVID-19 pandemic caused many CCEE programs to close temporarily. 1,2 Programs that remained open or reopened during the pandemic functioned differently due to health and safety precautions (e.g., visitors were not allowed, children’s temperatures were taken at the door, masks may have been used, social distancing was observed). 3,4 Staffing shortages at CCEE programs during the pandemic also forced programs to reduce the number of children they were serving and care hours (e.g., programs opening later or closing earlier). 5-7 CCEE staff and families of children at CCEE programs also faced increased financial insecurity and tolls on their physical and mental health. 8-11

During the pandemic, CCEE researchers, often in the middle of research studies, had to make difficult decisions about if and how to move forward with their projects. As part of the Child Care and Early Education Policy and Research Analysis (CCEEPRA) project with the Office of Planning, Research, and Evaluation (OPRE), Child Trends hosted a virtual convening in April 2023 with researchers from nine OPRE-funded research projects. See Table 1 and Appendix A for a list of these projects. The purpose of the convening was to learn about the implications of researchers’ revised research processes and methods during the COVID-19 pandemic for participants and study findings. Another purpose of the convening was to ask, “Which of the revised methods and processes make sense to continue into the future because they could improve the experience of participants and the quality of the research findings?”

The key themes and future considerations that emerged from this virtual convening are not comprehensive or representative. The nine projects selected for the convening are a sample of OPRE-funded CCEE research projects. This brief shares these researchers’ experiences and reflections to inform the broader CCEE research field’s work moving forward. Many of the changes researchers made to their processes and methods, as well as their ideas for future considerations for CCEE research about flexibility and increasing equity, are not novel or relevant solely to the COVID-19 pandemic. However, conditions in CCEE during the COVID-19 pandemic heightened the need for researchers to adapt their protocols in creative ways that have the potential to advance the CCEE research field.

View brief on the Office of Planning, Research and Evaluation website

This brief is part of the Child Care and Early Education Policy and Research Analysis (CCEEPRA) project . CCEEPRA supports policy and program planning and decision-making with rigorous, research-based information.

1 Lin, Y. & McDoniel, M. (2023). Understanding child care and early education program closures and enrollment during the first year of the COVID-19 pandemic. OPRE Report #2023-237. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. https://www.acf.hhs.gov/opre/report/understanding-child-care-and-early-education-program-closures-and-enrollment-during

2 National Association for the Education of Young Children. (2020). Holding on until help comes. https://www.naeyc.org/sites/default/files/globally-shared/downloads/PDFs/our-work/public-policy-advocacy/holding_on_until_help_comes.survey_analysis_july_2020.pdf

3 Lin, Y. & McDoniel, M. (2023). Understanding child care and early education program closures and enrollment during the first year of the COVID-19 pandemic. OPRE Report #2023-237. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. https://www.acf.hhs.gov/opre/report/understanding-child-care-and-early-education-program-closures-and-enrollment-during

4 Shaw, S., Franchett, A., LaForett, D., Maxwell, K., & Bultinck, E. (2023). Head Start’s response to the COVID-19 pandemic. OPRE Report #2023-025. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. https://www.acf.hhs.gov/opre/report/head-starts-response-covid-19-pandemic

5 National Association for the Education of Young Children. (2020). Holding on until help comes. https://www.naeyc.org/sites/default/files/globally-shared/downloads/PDFs/our-work/public-policy-advocacy/holding_on_until_help_comes.survey_analysis_july_2020.pdf

6 Shaw, S., Franchett, A., LaForett, D., Maxwell, K., & Bultinck, E. (2023). Head Start’s response to the COVID-19 pandemic. OPRE Report #2023-025. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. https://www.acf.hhs.gov/opre/report/head-starts-response-covid-19-pandemic

7 Tout, K. (2021). Child care and COVID-19: Support children by investing in early educators and program sustainability. Society for Research in Child Development. https://www.srcd.org/sites/default/files/resources/FINAL_SRCDCEB-ECEandCOVID.pdf

8 Lin, Y. & McDoniel, M. (2023). Understanding child care and early education program closures and enrollment during the first year of the COVID-19 pandemic. OPRE Report #2023-237. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. https://www.acf.hhs.gov/opre/report/understanding-child-care-and-early-education-program-closures-and-enrollment-during

9 Patrick, S. W., Henkhaus, L. E., Zickafoose, J. S., Lovell, K., Halvorson, A., Loch, S., Letterie, M., & Davis, M. M. (2020). Well-being of parents and children during the COVID-19 pandemic: A national survey. Pediatrics, 146(4). http://doi.org/10.1542/peds.2020-016824

10 Shaw, S., Franchett, A., LaForett, D., Maxwell, K., & Bultinck, E. (2023). Head Start’s response to the COVID-19 pandemic. OPRE Report #2023-025. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. https://www.acf.hhs.gov/opre/report/head-starts-response-covid-19-pandemic

11 Tout, K. (2021). Child care and COVID-19: Support children by investing in early educators and program sustainability. Society for Research in Child Development. https://www.srcd.org/sites/default/files/resources/FINAL_SRCDCEB-ECEandCOVID.pdf

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Open access
Published: 09 September 2024

A quantitative content analysis of topical characteristics of the online COVID-19 infodemic in the United States and Japan

Matthew Seah 1 &
Miho Iwakuma 1

BMC Public Health volume 24 , Article number: 2447 ( 2024 ) Cite this article

Metrics details

The COVID-19 pandemic has spurred the growth of a global infodemic. In order to combat the COVID-19 infodemic, it is necessary to understand what kinds of misinformation are spreading. Furthermore, various local factors influence how the infodemic manifests in different countries. Therefore, understanding how and why infodemics differ between countries is a matter of interest for public health. This study aims to elucidate and compare the types of COVID-19 misinformation produced from the infodemic in the US and Japan.

COVID-19 fact-checking articles were obtained from the two largest publishers of fact-checking articles in each language. 1,743 US articles and 148 Japanese articles in their respective languages were gathered, with articles published between 23 January 2020 and 4 November 2022. Articles were analyzed using the free text mining software KH Coder. Exploration of frequently-occurring words and groups of related words was carried out. Based on agglomeration plots and prior research, eight categories of misinformation were created. Lastly, coding rules were created for these eight categories, and a chi-squared test was performed to compare the two datasets.

Overall, the most frequent words in both languages were related to health-related terms, but the Japan dataset had more words referring to foreign countries. Among the eight categories, differences with chi-squared p ≤ 0.01 were found after Holm-Bonferroni p value adjustment for the proportions of misinformation regarding statistics (US 40.0% vs. JP 25.7%, ϕ 0.0792); origin of the virus and resultant discrimination (US 7.0% vs. JP 20.3%, ϕ 0.1311); and COVID-19 disease severity, treatment, or testing (US 32.6% vs. JP 45.9%, ϕ 0.0756).

Conclusions

Local contextual factors were found that likely influenced the infodemic in both countries; representations of these factors include societal polarization in the US and the HPV vaccine scare in Japan. It is possible that Japan’s relative resistance to misinformation affects the kinds of misinformation consumed, directing attention away from conspiracy theories and towards health-related issues. However, more studies need to be done to verify whether misinformation resistance affects misinformation consumption patterns this way.

Peer Review reports

Introduction

The COVID-19 pandemic has brought into the spotlight the growing infodemic : the “excessive amount of unfiltered information concerning a problem such that the solution is made more difficult” [ 1 ]. Between the mainstream media, statements made by politicians, social media platforms, instant messaging services, and changing guidelines released by official institutions, the typical person is constantly inundated with a barrage of information that presents both the challenge of discerning reliable information, as well as the option to take fringe or pseudoscientific theories as the truth. This represents a public health concern, as COVID-19 misinformation or “fake news” may spread anti-vaccine views or promote racial discrimination [ 2 ].

A multi-pronged approach is necessary to mitigate the impact of the infodemic, as no single intervention can achieve the breadth required to match the scale of the worldwide flow of information. Eysenbach proposes four pillars of infodemic management in his 2020 paper: infoveillance and infodemiology (surveillance of information supply and demand, as well as its quality); building eHealth literacy; improving the translation of knowledge between academia and larger outlets such as policymakers, mainstream media, and social media; and the peer-review process and fact-checking [ 3 ].

“Fact-checking” refers to the process of evaluating a statement for its factual accuracy or whether it has been framed in a misleading manner due to omission of context. Fact-checking has its origins in American TV segments devoted to checking the accuracy of statements made by American presidential candidates [ 4 ], though most current fact-checking content is produced by websites such as Snopes or FactCheck.org in the form of articles or videos.

Fact-checking alone cannot be the ultimate counter to misinformation – not only does it have limited effects on correcting perceptions of misinformation due to the strong biases and emotions involved when interacting with such information [ 4 , 5 ], the local politics of truth [ 6 ], i.e. the historical and cultural contexts of the region, inform behavior and beliefs to a significant degree; for instance, close-contact burial practices in parts of west Africa stricken by ebola [ 7 ], or vaccine hesitancy in Japan following the HPV vaccine scare in 2013 [ 8 ]. Interventions targeting an infodemic need to take into account the nature and context of the region to be effective.

One of the few extant studies comparing the COVID-19 infodemics and national contexts across countries was published by Zeng et al. [ 9 ], in which they analyzed fact-checking article contents from the US, China, India, Germany, and France. Some key findings included the fact that non-health misinformation (e.g. regarding politics, or the origin of the virus) is nearly twice as common as health misinformation (e.g. COVID-19 being “just a cold”); Germany is relatively resilient to misinformation compared to the US or India owing to its low societal polarization and high trust in the news media; misinformation regarding the spread of COVID-19 or travel restrictions is common in China, likely due to China being the early epicenter of the pandemic as well as large-scale travel movements that occur around Chinese New Year; and wedge-driving misinformation along religious lines is common in India owing to the longstanding conflict between the nation’s Muslim and Hindu populations.

Although there is already an abundance of cross-cultural research between the US and Japan, a comparative study of infodemics in these countries has yet to be done, and much has changed in the time since the publication of the Zeng paper – noteworthy developments including the progress made in global vaccination campaigns [ 10 ], and the emergence of the highly transmissible delta and omicron variants [ 11 ]. Furthermore, the national contexts of the US and Japan differ to a notable extent, in geographical, sociocultural, and historical terms, making it reasonable to expect differences in the types of misinformation that would gather more traction. Therefore, this research aims to provide an updated understanding of the COVID-19 infodemics in the US and Japan through a quantitative content analysis of the types of misinformation that appear in fact-checking articles.

Methodology

Data selection and gathering.

In order to find the types of COVID-19 misinformation that gathered significant traction in the US and Japan, COVID-19 fact-checking articles were gathered from the top two largest fact-checking publishers: Politifact and FactCheck.org for the US, and Buzzfeed and InFact for Japan. All articles were written in their respective countries’ languages (English for the US, Japanese for Japan). A summary of the data sources used is shown in Table 1 below. Articles included were published between 23 January 2020 and 4 November 2022.

Article URLs were scraped from the COVID-19 sections of each source in Python, using the Selenium library in Chrome 108.0.5359.124. Following this, a separate program was used to visit the listed URLs and scrape the article contents using the news-please library [ 16 ]. (Source codes can be accessed at https://github.com/seahmatthew/KyotoU-PublicHealth2023 .)

Data analysis in KH coder

The open-source quantitative text analysis program KH Coder [ 17 ], developed by Koichi Higuchi at Ritsumeikan university, was used to analyze the article contents, with the US and Japan datasets in separate projects. As of January 2023, there are 5,761 published research articles which make use of KH Coder [ 18 ], many of which cover health-related research topics. Its strengths include functions for statistical analysis (e.g., term frequency) of large data files, as well as the KWIC Concordance function [ 19 ] which provides the capability to easily refer to the original data from any given result.

Word Frequency [ 19 ] was used to obtain an overview of the data as a preliminary step. Following this, Hierarchal Cluster Analysis [ 19 ] was used to explore groups of related words, and also to build the lists of terms to force pickup (such as “toilet paper” or “Moderna”) which would not be picked up by default, and irrelevant terms to force ignore (such as “website” or “article”), which introduce noise due to appearing very frequently but not being indicative of any relevant themes. This took a process of trial and error especially when building the force ignore lists, as blocking certain seemingly irrelevant terms would sometimes turn out to hide an otherwise useable article.

After substantive force pickup/ignore lists had been built for each languages, the lists were compared to ensure that relevant keywords were ignored in both languages, although words that appear frequently as syntactic features in each language (such as “pants [on] fire” or “subject”) were not duplicated in the same way.

Next, Hierarchal Cluster Analysis was re-run using the finalized force pickup/ignore lists to gather the terms to form the document coding files. For the U.S. dataset, the minimum Term Frequency (TF) was set to 90, Document Frequency (DF) to 1, and only nouns, proper nouns, and terms from the force pickup list were analyzed to minimize noise. For the Japan dataset, the minimum TF was set to 10, DF to 1, and only nouns, proper nouns, location names, and terms from the force pickup list were analyzed. For both datasets, the Ward method and Jaccard frequency were used, with the number of clusters shown being auto-chosen.

Based on the agglomeration plot turning points from the Hierarchal Cluster analyses, the prior Zeng paper [ 9 ], and familiarity with the data, it was decided to split the data into eight categories. From the categories and keywords found, coding files were built for the US and Japan datasets and applied to obtain the frequencies for each category. Articles could be assigned to multiple categories, and manual sorting was used to classify articles through a first pass after automatic sorting. Articles that failed to be classified in any category after both automatic and manual sorting were assigned to a separate Miscellaneous category.

After the code frequencies for each language had been obtained, chi-squared tests were carried out to test whether there were differences in the frequencies across countries. Holm-Bonferroni adjustment was used to adjust the p values.

The agglomeration plots produced from the Hierarchal Cluster analyses are shown below in Fig. 1 . The turning points show that somewhere in the range of seven categories would be ideal, but considering prior research and familiarity with the data, it was decided to generate eight categories.

Agglomeration plots produced by Hierarchal Cluster Analysis of the US (left) and Japan (right) datasets

The coding files created based on the categories and keywords found are shown in Table 2 . A total of eight categories were created: government policy; resource shortages; statistics; measures to stem the spread of infection; masks and transmission; origin of the virus and resultant discrimination; COVID-19 disease severity, treatment, or testing; and vaccine efficacy, contents, or safety. Each category contains a set of keywords in its respective language that results in close association; for instance, “lockdown”, “quarantine”, and “border” associate highly with articles about measures taken to stem the spread of infection.

A summary of the top 50 words with the highest tf (term frequency) is shown in Table 3 . Both the U.S. and Japan lists are topped by words pertaining to vaccination, masks, cases and testing, likely because these words are likely to appear across a broad range of categories. For instance, words pertaining to vaccination could appear in both articles about supposed deleterious health effects of vaccination, as well as articles about vaccination program plans or vaccine-related conspiracy theories.

A summary of the code frequencies, chi-squared test p values, and relevant excerpts from the data is provided below in Table 4 . Articles that contained none of the eight predetermined codes are grouped in the “Miscellaneous” category. Chi-squared tests were carried out to compare the code frequencies across datasets, and p value correction was done using the Holm-Bonferroni method. Three categories stood out due to their relatively low p values and relatively high effect sizes: statistics, the origin of the virus and resultant discrimination, and COVID-19 severity, treatment, and testing.

Versions of Tables 2 and 3 , and 4 with the original Japanese text are available in Supp_012024.docx.

The effect sizes ϕ for each category are shown below in Table 5 . Only the category on the origin of the virus and resultant discrimination showed an effect size exceeding 0.1, a small effect. The two categories of statistics, and COVID-19 severity, treatment, and testing showed the next-highest effect sizes of > 0.07. Hence, these three categories were chosen for further discussion.

Similarities and differences between US and Japan categories

Selective reading of articles with high tf (term frequency) for the chosen categories produced a handful of similarities and differences. Within the statistics category (which was more common in the US dataset, 40.1% vs. 25.7%, ϕ 0.0792), misinformation from both countries tended to downplay the severity of the COVID-19 mortality rate, or otherwise make factually false statistical assertions. US misinformation tended to make more (invalid) comparisons to influenza, and there were false assertions that the US was performing statistically better in terms of mortality rate than other countries, while Japanese misinformation contained more assertions that vaccines increase mortality rate. Many of the US articles in this category were based on quotes from then-President Donald Trump.

Within the category regarding the origin of the virus and resultant discrimination (which was more common in the Japan dataset, 20.3% vs. 7.0%, ϕ 0.1311), misinformation from both countries asserted that COVID-19 was artificially made in the Wuhan Institute of Virology. However, US misinformation tended to focus on federal funding for the institute, and some articles tied the origin of the pandemic to Chinese meat-eating practices. Japanese misinformation focused more on Chinese people within Japan itself, such as warning of incoming tourist swarms or Chinese nationals taking up space in hospitals.

Within the category of COVID-19 severity, treatment, or testing (which was more common in the Japan dataset, 46.0% vs. 32.6%, ϕ 0.0756), both countries had misinformation about treatments for COVID-19, as well as about testing kits. While both countries mentioned ivermectin, hydroxychloroquine and marijuana as COVID-19 treatments were exclusive to the US dataset, while green tea and hot water were exclusive to the Japan dataset. More US articles tended to downplay the severity of infection by likening it to the flu. There were pieces of misinformation in the US that stemmed from misinterpretation of test kits, while there were Japanese assertions that COVID-19 test kits are faulty or ineffective.

Overall, non-health misinformation appeared more frequently than health misinformation, echoing findings from other studies analyzing fact-checking articles [ 9 ] or social media posts [ 20 ].

In addition, while the category frequencies for masks and transmission did not appear to differ, the contents of articles in these categories showed differences: articles from the US dataset tended to be regarding misinformation on the effectiveness of masks as a means for preventing transmission, while articles from the Japan dataset tended to be on ancillary topics, such as the country of manufacture of masks, or mask shortages. Mask-wearing as a means for preventing disease transmission while sick is an established aspect of Japanese culture [ 21 ].

National contextual factors that affect misinformation consumption

As outlined above, there are some differences in the contents of the COVID-19 misinformation circulating in the US and Japan. A few of the numerous contextual factors that may have influenced these differences will be described further below.

Importantly, it should not be assumed that a cause-and-effect relationship is at play, as a myriad of factors influence consumer (and macro-level) information-seeking habits. For instance, on the micro level, there are consumer culture factors that influence patterns of consumption, such as social influences or social class [ 22 ]; on the macro level, society-level factors such as the quality of official communications can affect attitudes towards health measures [ 23 ]. Some evidence also exists to suggest that in certain countries, the demand for certain kinds of misinformation fluctuates based on the epidemic curve [ 9 ]. While a comprehensive list of every potential influencing factor would be beyond the scope of this research, it can be seen that local context can indeed influence information-seeking habits. Understanding the concerns and mindsets of those grappling with the infodemic should be a priority in determining what countermeasures to take (e.g., targeted messaging, rapid response, etc.).

On the topic of the high prevalence of political figures involved in US misinformation, a survey conducted by the Reuters Institute for the Study of Journalism in 2020 [ 24 ] found that American information-seeking habits surrounding COVID-19 are strongly tied to political affiliation. Left-leaning respondents were likely to trust the news media and unlikely to trust the government; the opposite was true for right-leaning participants. Trump was himself a major direct source of COVID-19 misinformation [ 25 ], and many of the erroneous claims he made are reflected in the data, especially in the Statistics and Origin categories. The significant sway a person’s political beliefs hold over their information-seeking behavior in the US is likely to be associated with the country’s highly polarized political climate. This finding of the high frequency of misinformation from politicians in the US is echoed in the Zeng paper [ 9 ], and the same paper found that this connection between societal polarization and political misinformation was also clear in India.

In the Japanese dataset, articles pertaining to the origin of COVID-19 from China were much more frequent and pointed in general; as opposed to US articles which mostly addressed conspiracy theories of American funding for the Wuhan Institute of Virology or the animal origins of the virus, articles in this category in the Japan dataset tended to focus directly on Chinese nationals, either as disproportionate occupants of Japanese medical institutions, or as spreaders of COVID-19 inbound from China. Japan’s relative geographical proximity to China and popularity as a Chinese tourist destination, as well as existing anti-Chinese sentiment that has been worsening progressively since the 1980s [ 26 ], may explain to some extent the personal nature of Japanese misinformation in this category.

At first glance, it may seem surprising that both the US and Japan have similar proportions of articles discussing vaccine efficacy, contents, or safety, especially given the heavy role US political figures played in leading supporters to act contrary to evidence-based findings [ 27 ]. In an article published in the Japanese journal Chiryo in 2021, the founders of HPV vaccine awareness group MinPapi describe how vaccine hesitancy in Japan may have been exacerbated by the human papillomavirus (HPV) vaccine side effect scare in 2013 [ 28 ]; years later, addressing vaccine hesitancy through their new website CoviNavi continues to be a challenge.

Additionally, a 2021 survey conducted in Japan showed that Japanese respondents were uncertain in general about what sources of COVID-19 information they could trust [ 20 ]. 24.7% of respondents believed there was no information source they could trust, and only 26.0% of respondents felt they could trust health experts. This stands in stark contrast to the results from the aforementioned Reuters study, where over 80% of American respondents on both sides of the political spectrum felt they could trust health experts. This difference in response to the infodemic – picking sides, as opposed to being assailed by uncertainty – may actually help to explain why vaccine misinformation is relatively common in both countries; one possible interpretation is that a limited segment of the American audience consumes vaccine misinformation in greater per capita amounts, while a more general segment of the Japanese audience consumes vaccine misinformation in lower per capita amounts.

Disinformation resilience and its effects on misinformation consumption

In a 2020 paper, Humprecht et al. outline a framework for cross-national comparisons of disinformation (henceforth “misinformation”) resilience : the degree to which online misinformation is likely to receive exposure and be spread [ 29 ]. Political factors limiting misinformation resilience include societal polarization, and frequency of populist communication; media-related factors include low trust in news media, weak public news services, and audience fragmentation; economic factors include a large advertisement market size, and high social media usage. Using this framework in a comparison of the US with 16 other mainly European countries, the authors found that the US scored the lowest in misinformation resilience, owing to its fragmented media landscape, large ad market, low trust in news, highly polarized society, and frequent populist communication.

In comparison to the US, Japan scores notably lower in terms of populist communication [ 30 ]; NHK, the public broadcasting network, attains comparable viewership to other networks [ 31 ] as opposed to American public broadcasters with one- to two-thirds the viewership of major American TV networks [ 32 , 33 ]; major TV news networks in Japan attain roughly two times the viewer share of US TV network providers, with Yahoo! News dominating the online news market with over 50% weekly usage [ 34 ]. While a formal comparison has yet to be done in the literature, these factors suggest that Japan may be more resilient to misinformation than the US. It is possible that this affected the sizes of the datasets that could be obtained, leading to the US dataset being more than ten times as large than the Japan dataset.

While it stands to reason that increased misinformation resilience would lead to lower spread and consumption of misinformation, its effect on the types of misinformation consumed is less clear. In the Zeng study [ 9 ], Germany stood out as one of the studied countries with high misinformation resilience; compared to the other countries which tended to contain high proportions of articles on political conspiracy theories, lockdown measures, or transmission methods, misinformation from Germany was centered on COVID-19 treatment and vaccines, similarly to the Japan dataset used in this report. If we consider the nature of rumors and misinformation as an answer-seeking response to a perceived external threat [ 35 ], one possible interpretation of this pattern is that increased misinformation resilience in the midst of the pandemic contributes to lower distraction with non-key issues – the key issue in this context being the health impact of COVID-19 and how it can be avoided or treated. The “Miscellaneous” category is mostly comprised of articles on these non-key issues , including those bordering on absurdity or conspiracy; while this category was not notably differently sized between the US and Japan datasets, the Japan data had a noticeably lower proportion of misinformation along the lines of the “deity of death” US article.

Strengths and limitations of this study

In comparison to prior studies which used fact-checking articles as data, this study uses a larger sample size for the US dataset and offers a Japanese dataset for the first time. In particular, using KH Coder allowed for multiple categories to be assigned to a single article, which reflects the data more accurately than other studies [ 9 ] that are limited to a single category for each article. Additionally, quantitative content analysis using KH Coder allowed for counting the term frequencies in the large datasets, as well as for referring back to the original data when needed using the KWIK Concordance function.

However, as to the limitations of the study, the span of misinformation covered in this report is limited to that selected by the editorial teams in a “gatekeeping” process [ 36 ] for the four online news sources used; in particular, fact-checking in Japan is a relatively new endeavor, with the InFact team and website notably smaller than established fact-checking organizations from the US. This has negative implications for the generalizability of the Japan data, and a larger future dataset would likely give richer results. In addition, since the categorization processes were carried out automatically, there may be a handful of data points that have not been categorized correctly. More studies should be done to further verify the relationship between the misinformation resistance of a country and the types of misinformation that spread within it. Future studies of this nature will have larger and more varied datasets to work with, whether they are about COVID-19 or any other infodemic. Finally, the effect sizes found for the sections discussed here are all of small magnitude, meaning that it should not be inferred that certain segments of misinformation should receive disproportionate amounts of focus in countries that seem vulnerable to that kind of misinformation.

Practical implications

In combination with aggregated data from other countries, data on the types of misinformation which are comparatively common in the country provides policymakers a reference point when allocating resources to tackling misinformation, through means such as rapid-response messaging [ 37 ]. Of course, this data should be weighed against the actual likely impact of said misinformation spreading in the populace; any given piece vaccine misinformation is likely to do more harm overall than a wild claim of a vaccination center bearing a logo of a “deity of death”.

This research also opens up new avenues for further research – for instance, research to verify whether modifying our taking a culturally-relevant approach to tackling misinformation results in better correction outcomes. One possible example would be altering the tone of messaging to be firmer and more succinct in an environment like Japan, where misinformation likely spreads out of uncertainty instead of certainty in misinformation, while a more indirect approach may be more effective in places like the United States where misinformed beliefs are grounded in certainty.

Using quantitative content analysis, this study shows the similarities and differences in the COVID-19 infodemics in US and Japan since the start of the pandemic. Differences were found in the proportion of articles mentioning statistics, the origin of the virus and resultant discrimination, and COVID-19 severity, treatment and testing, though the effect sizes were seen to be small.

Several facets of national context appear to support the trends seen in the data, such as the history of the HPV vaccine in Japan leading to increased distrust of COVID-19 vaccines. In addition, application of a misinformation resilience framework appears to show that in countries with higher resilience, distracting non-key issues such as conspiracy theories attract less attention compared to key issues , which refer to COVID-19 health impacts and other health information in the context of the pandemic. Understanding the types of misinformation in circulation gives policymakers and educators direction in developing strategies to counter this misinformation.

Lastly, it should be reiterated that fact-checking, even when done through appropriate channels in a culturally relevant manner, cannot be relied upon as the sole measure with which to combat an infodemic. Not only does fact-checking have heavily limited effects on correcting misinformed beliefs [ 4 , 5 ], a deluge of fact-checking information may even backfire by contributing to information overload and avoidance in the intended audience [ 38 ], or by simply acting as a dissemination channel for the misinformation that would not have been spread otherwise [ 36 ]. Fact-checking has a place as one of the pillars of infodemic management – there is a need to uphold journalistic integrity, and to provide a reliable source for a more invested, informed reader subset. The other pillars of infoveillance and infodemiology, the gradual process of building eHealth literacy in the populace, and providing clear, timely translations of scientific findings to actionable messages need to be upheld in tandem as a long-term strategy for decreasing the impact of misinformation [ 3 ].

Data availability

The dataset supporting the conclusions of this article is available in the GitHub repository, https://doi.org/10.5281/zenodo.8282744 at https://github.com/seahmatthew/KyotoU-PublicHealth2023 [ 39 ].

Abbreviations

Coronavirus disease 2019

Human papillomavirus

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Seah, M., Iwakuma, M. A quantitative content analysis of topical characteristics of the online COVID-19 infodemic in the United States and Japan. BMC Public Health 24 , 2447 (2024). https://doi.org/10.1186/s12889-024-19813-y

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The COVID-19 research landscape

Measuring topics and collaborations using scientific literature.

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∗Correspondence: Na Hong, Digital China Health Technologies Co. Ltd., Beijing 100080, China (e-mail: [email protected] ).

How to cite this article: Wang J, Hong N. The COVID-19 research landscape: Measuring topics and collaborations using scientific literature. Medicine . 2020;99:43(e22849).

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Objectives:

Method:

Results:

Conclusions:

1 Introduction

A novel coronavirus emerged and caused a rapid spread of phenomena in Wuhan, China, at the end of 2019. In February 11, 2020, the World Health Organization named this disease Coronavirus Disease 2019 (COVID-19). [1] With the global spread of COVID-19, it threatened human lives, caused heavy burdens, and brought tremendous challenges to social development. To support the public health decision-making and scientific countermeasures implementation, researchers around the world were racing to study on the disease transmission, diagnostic tests, treatments, vaccines, among others. With the joint efforts of researchers and clinicians around the world, more and more COVID-19-related articles have been published and the outputs of scientific research are constantly emerging. As of July 1, 2020, PubMed has included 27,370 published articles on COVID-19.

State of the art literature review about COVID-19 demonstrated that most available literature-based studies could be basically divided into 2 kinds. The first kind is systematic reviews or meta-analyses. Most of them focused on a certain specific subfields of COVID-19 research, such as drug therapy, diagnostic methods, or clinical symptoms. For example, Alzghari et al [2] performed a systematic review to investigate the effect of Tocilizumab on COVID-19, and Zhu et al [3] systematically reviewed the CT imaging features of COVID-19 to provide reference for clinical practice. The second kind is the bibliometric analysis which uses quantitative analysis methods to describe literature in a particular research domain. However, some of the bibliometric analysis were targeting at coronavirus, not just severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), for the purpose of providing reference for COVID-19 research, and the time window was usually set for a long retrospective duration. [4–7] For example, Mao et al [7] analyzed coronavirus articles published from 2003 to 2020. Up to the investigation time of this study, there were limited number of bibliometric studies specific to COVID-19 and most of them were found and implemented at early stage of COVID-19 outbreak. [8,9] For example, Lou et al [8] executed a query in PubMed using keyword “COVID-19” and analyzed 183 related articles. Most of these previous literature-based studies of COVID-19 provided a specific review for COVID-19 research progresses or clinical observations; however, the description of a whole picture of COVID-19 scientific research using systematical methods was still insufficient.

2 Materials and methods

2.1 data source.

The data scope of this study is COVID-19-related articles published from January 1, to July 1, 2020. Since PubMed has served as the primary database for retrieving biomedical literature, it was selected as the only data source. [10] Ethical approval was not required because no human and animal subjects were enrolled.

2.2 Search strategy

2.3 Data collection

2.4 Bibliometric and visualized analysis

MTI (National Library of Medicine, Bethesda, MD), [11] VOSviewer 1.6.15 (Leiden University, Leiden, Netherlands) [12] and D3.js (Mike Bostock, Observable, Inc., San Francisco, CA) [13] were used to carry out bibliometric and visual analysis of the publications. Since Medical Subject Headings (MeSH) represent much richer semantics that author-selected keywords, they were chosen as the object of topic analysis. MTI was used to extract MeSH terms from title and abstract of articles because newly created articles in PubMed will not be indexed with MeSH terms immediately. VOSviewer was used to generate collaborative network of countries/regions/institutes and co-occurrence network of MeSH terms. Finally, D3.js was used to visualize the internal hierarchy and the popularity trend of topics, which identified by MeSH terms co-occurrence clustering.

2.5 Analytical methods

3.1 The Scale of COVID-19 publications

The number of COVID-19 research publications has been continuously increasing after its break. According to the growth trend from the view of global to country level, as shown in Figure 1 , United States overtook China Mainland as the largest contributor in publishing COVID-19-related articles in early May 2020. As of July 1, 2020, United States had published 5949 (21.7% of the total) articles, and China Mainland had published 4080 (14.9% of the total) articles in total that are much higher than any of the other countries. The following Italy (10.7%) and UK (8.4%) were also prolific among the top 10 countries ( Table 1 ). In addition, China Mainland had the highest rate of domestic collaboration (79.4%), whereas Australia had the lowest (34.8%) among the top 10 productive countries.

3.2 The collaborative network of countries/regions

Collaboration activities on country/region level were measured based on co-author analysis. As shown in Figure 2 , there were 76 countries/regions involved in COVID-19 research collaboration which divided into 3 clusters.

3.3 The collaborative network of research institutes

The most productive institutes were located at United States, China Mainland, and Europe. There were 307 institutes that had published >10 articles. Table 2 lists the number of publications and internal collaboration publications for top 10 productive institutes. Huazhong University of Science and Technology (523), Wuhan University (340), and University of California (300) were ranked as Top 3 productive institutes by number of publications. Besides, the BMJ editors published 193 latest news and comments about COVID-19 research with the highest rate of internal collaboration of 100%.

Collaboration network among productive institutes was generated based on co-author analysis. Institutes were clearly separated into 5 clusters as shown in Figure 3 . Cluster 1 (red color) included 96 institutes which were mostly universities and hospitals of United States, as well as 10 universities from Canada, among which University of Toronto ranked as Top 1 institute for global research collaboration with the largest number of total link strength. Besides, University of California and University of Washington were also the collaboration centers with large number of co-authored articles. The universities, hospitals, and research institutes came from China composed Cluster 2 (blue color), from which Huazhong University of Science and Technology and Wuhan University had the largest number of link strength compared with other institutes, with a total of 60 collaboration papers. Furthermore, >100 institutes from Europe composed Cluster 3 (green color) and Cluster 4 (yellow color), of which universities and hospitals from Italy composed Cluster 4 and the remaining institutes composed Cluster 3. According to co-author analysis on these 2 clusters, University College London and University of Oxford were most active on research collaboration with other institutes. In addition, it was interesting to observe that Cluster 5 (purple color) contributed a relatively small volume of publications but was a self-centered research community mainly composed with 8 universities from Iran.

3.4 The identified COVID-19 research topics

To achieve better understanding of what are the researcher's focuses and research progress of COVID-19 with its break timeline, MeSH terms of each article were selected as the observation objects to measure the research topics and topic trends. On the analysis of selected 2000 MeSH terms with their frequency above 10, a MeSH terms co-occurrence network with 584 high-frequency terms were generated, as shown in Figure 4 . The network center nodes are COVID-19, severe acute respiratory syndrome coronavirus 2, and Coronavirus Infections. Four topics about COVID-19 research were obviously identified: epidemiology and public health interventions, virus infection and immunity, clinical symptoms and diagnosis, drug treatments, and clinical studies, as shown in Figure 5 .

3.4.1 Topic I: epidemiology and public health interventions

The research topic of epidemiology and public health interventions had gathered great attentions. It contained 281 of the 584 MeSH terms, indicating that the prevention and control of COVID-19 was the most concerned issue at all the stages of disease break. It mainly contained epidemic transmission dynamics, prevention and control measures and effect analysis at different regional levels (global, national, and urban), [14,15] epidemiological investigation, modeling, and trend prediction from the perspective of public health, [16,17] as well as various personal protective measures (Disinfection, Hand Hygiene, Masks, Personal Protective Equipment, Protective Devices), [18,19] and social prevention and control measures (Airway Management, Mass Screening, Social Distance, Social Isolation). [20] In addition, high attention had been paid to the psychological and mental state (Anxiety, Anxiety Disorders, Depression, Fear, Mental Disorders, Mental Health) of the general public, infected people, and medical workers. [21]

3.4.2 Topic II: virus infection and immunity

A total of 168 MeSH terms were included in this topic, which was mainly for the molecular biology and immunology studies of SARS-CoV-2 for the purpose of detection and prevention. Three subtopics of Topic II were identified based on content analysis. The first subtopic was the research on the pathogenesis of COVID-19 that included the replication process and infection mechanism of SARS-CoV-2 in human cells, with emphasis on the interaction between SARS-CoV-2 and biological enzymes (RNA-directed DNA polymerase, angiotensin-converting enzyme [ACE2], serine endopeptidases). [22,23] The second subtopic was the studies on the etiological detection methods of SARS-CoV-2 and the most important methods involved were real-time polymerase chain reaction and reverse transcriptase polymerase chain reaction (PCR). [24,25] In addition, COVID-19 vaccine development with the aim of inducing immune response composed the third subtopic. [26,27]

3.4.3 Topic III: clinical symptoms and diagnosis

A total of 111 MeSH terms were included in Topic III, which mainly covered clinical symptoms of COVID-19 patients and various testing methods used for diagnosis. The clinical symptoms (or complications) of COVID-19 mentioned in the literature mainly included: abdominal pain, cough, diarrhea, dyspnea, fatigue, fever, headache, leukopenia, lymphopenia, myalgia, nausea, pharyngitis, pleural effusion, pneumonia, pulmonary embolism, respiratory distress syndrome, respiratory insufficiency, vomiting, among others. [28,29] The diagnostic methods, mostly discussed in the literature, were routine blood tests (alanine transaminase, aspartate aminotransferases, biomarkers, C-reactive protein, leukocyte count, l -lactate dehydrogenase, lymphocyte count, neutrophils, platelet count) and imaging examinations (radiography, tomography, x-rays). [30]

3.4.4 Topic IV: drug treatments and clinical studies

Topic IV contained 24 MeSH terms, which was the smallest topic. The research content in this topic was mainly in vivo and in vitro trials of multiple drugs and their combinations for the purpose of treating COVID-19. The studied drugs involved antibacterial/antiviral drugs (azithromycin, favipiravir, lopinavir, remdesivir, ribavirin, ritonavir), antimalarials, and rheumatoid arthritis drugs (chloroquine, hydroxychloroquine, tocilizumab) among others. Because of the difference of clinical endpoint and experimental design, the trials results obtained so far are not consistent. For example, some researchers conclude that remdesivir can be used as potent drugs against COVID-19 [31] ; however, some studies show that remdesivir cannot significantly improve the symptoms of patients with severe COVID-19. [32] Chloroquine and hydroxychloroquine are in a similar situation to remdesivir. [33,34] Therefore, there is still no widely accepted standard on specific drugs or the best drug treatment options of COVID-19. [35–37]

3.5 Topic popularities and evolvements about COVID-19 research

Topic popularity of the above 4 COVID-19 topics was measured by using proportional frequency equation in Section 2, and the measured results, as shown in Figure 6 , were consistent with manually validation results by reviewing literature. According to trend analysis, the topic of epidemiology and public health interventions has gathered great attentions and continuously with high popularity. The characteristics of SARS-CoV-2, such as biological structure, genetic sequence, and infection mechanism, have been well studied, and beyond this, consensus has been reached on COVID-19 clinical symptoms and diagnostic methods.

On the topic tracking analysis of epidemiology and public health interventions, we found that most of the early studies and reports were mainly focus on China's epidemic prevention and control. [38,39] By implementing a series of preventive control and medical treatment measures, the pandemic in China had been effectively contained, but the number of confirmed cases outside China continued to increase, as did the corresponding research on epidemiology and public health interventions, which was consistent with the continuously high popularity trending curve of this topic (blue curve), as displayed in Figure 6 .

For virus infection and immunity study, the topic popularity decreased since early of February 2020. As studying the etiological characteristics of a novel virus, such as biological structure, genetic sequence, and infection mechanism, is the key to pandemic prevention and control, the trend curve of Topic II was in the highest position in the pre-outbreak period (January 2020). With the joint efforts of scientists around the world, substantial progress had been achieved in the understanding of SARS-CoV-2. For example, the genetic sequencing of SARS-CoV-2 was performed by Chinese scientists on January 7, 2020 and the results were timely shared with the WHO on January 12, 2020. Furthermore, the infection mechanism of SARS-CoV-2, especially its relationship with ACE2 was identified, and specific diagnostic PCR tests were produced. [40,41] The above achievements were mainly completed in January and February 2020, starting from February, the trend curve of Topic II gradually declined. However, the curve will remain at a high level because more and more attentions have been paid to vaccine-related research. According to literature reports, there are more than 100 candidate vaccine projects targeting COVID-19 worldwide, and some of them have entered clinical trials. [42,43]

With the continuous increase of confirmed and treated cases, clinicians achieved deeper understanding about COVID-19. Since March 2020, there has been a global consensus on the symptoms and diagnostic criteria for COVID-19. [28,44] In addition, the seventh and final edition of “Diagnosis and Treatment Protocol of COVID-19,” issued by the National Health Commission of the PRC, was also released on March 3, 2020. [45] As a result, the trend curve of Topic III starts to smooth out since March 2020 ( Fig. 6 ).

Although lopinavir/ritonavir was recommended as antiviral drug by the first edition of “Diagnosis and Treatment Protocol of COVID-19” on January 16, 2020 at the beginning of the pandemic, the widespread interest in using antiviral drugs to treat COVID-19 began with a report of the first diagnosed patient who benefit from remdesivir in United States, which was published in NEJM on January 31, 2020. [46] Therefore, the trend curve of Topic IV in Figure 6 has risen slightly since February 2020. However, the minimal topic size and low trend curve suggest that drug therapy remains the weak point in the response to COVID-19.

4 Discussion and conclusion

4.1 Strengths and limitations

Publications on COVID-19 research were retrieved from PubMed, and the collaboration status and research trends of COVID-19 were measured via bibliometric and visualized analysis, which was considered to be relatively objective and comprehensive. Moreover, well curated MeSH terms were used as the object of topic analysis in this study, compared with author-selected keywords which were usually chosen by existing COVID-19-related bibliometric analysis. [4–7] Due to the limited number and randomness of author-selected keywords, the derived results, especially the co-occurrence analysis results, cannot reflect the real status of the COVID-19 research. Our MeSH terms-based methodology could better disclose the research topics and trends of COVID-19. However, limitations also exist in our research. On the one hand, PubMed was selected as the only data source, so some articles only indexed in other databases such as Web of Science and Scopus might be left out. On the other hand, for the sparisity reason of citation network of published COVID-19 articles, citation analysis has not been adopted in this study. In the future, studies based on citation analysis, such as identification of influential authors and highly-cited articles, will be conducted and included in our further analysis.

Author contributions

Conceptualization: Na Hong.

Data curation: Junhui Wang.

Software: Junhui Wang, Na Hong.

Visualization: Junhui Wang, Na Hong.

Writing – original draft: Junhui Wang, Na Hong.

Writing – review & editing: Junhui Wang, Na Hong.

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2021 Top 25 COVID-19 Articles
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Coronavirus disease (COVID-19) pandemic: an overview of systematic
The spread of the "Severe Acute Respiratory Coronavirus 2" (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [].The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [], causing massive economic strain ...
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SARS-CoV-2 is a positive-sense single-stranded RNA virus. It is contagious in humans and is the cause of the coronavirus disease 2019 (COVID-19). Profiling of T cell responses in the lungs of ...
Coronavirus (COVID-19) research
In addition to the articles highlighted below, Sage provides free full-text access to research through PubMed that contains COVID-19 related keywords in their title. Organizing, synthesizing, and expanding knowledge on all forms of trauma, abuse, and violence. The most recent advances across the breadth of the cellular and molecular neurosciences.
Coronavirus (COVID-19): The latest science & expert commentary
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Stanford Medicine scientists have launched dozens of research projects as part of the global response to COVID-19. Some aim to prevent, diagnose and treat the disease; others aim to understand how it spreads and how people's immune systems respond to it. Below is a curated selection, including summaries, of the projects.
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Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews

On behalf of the International Network of Coronavirus Disease 2019 (InterNetCOVID-19)

Conclusions

Methodology

Study design

Eligibility criteria

Information sources

Study selection

Data collection process

Quality assessment in individual reviews

Synthesis of results

Managing overlapping systematic reviews

Characteristics of included reviews

Population and study designs

Systematic review findings

Clinical symptoms

Diagnostic aspects

Therapeutic possibilities

Laboratory and radiological findings

Quality of evidence in individual systematic reviews

Availability of data and materials

Acknowledgments

Author information

Contributions

Corresponding author

Ethics declarations

Consent for publication

Competing interests

Additional information

Supplementary Information

Additional file 2: Appendix 2.

Additional file 3: Appendix 3.

Additional file 4: Appendix 4.

Additional file 5: Appendix 5.

Additional file 6: Appendix 6.

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Manage citations:

Global Trends in Highly Cited Studies in COVID-19 Research

The COVID-19 research landscape

Objectives:

Conclusions:

1. Introduction

2. Materials and methods

2.2. Search strategy

2.3. Data collection

2.4. Bibliometric and visualized analysis

2.5. Analytical methods

3.1. The Scale of COVID-19 publications

3.2. The collaborative network of countries/regions

3.3. The collaborative network of research institutes

3.4. The identified COVID-19 research topics

3.4.1. Topic I: epidemiology and public health interventions

3.4.2. Topic II: virus infection and immunity

3.4.3. Topic III: clinical symptoms and diagnosis

3.4.4. Topic IV: drug treatments and clinical studies

3.5. Topic popularities and evolvements about COVID-19 research

4. Discussion and conclusion

4.1. Strengths and limitations

Author contributions

COVID-19 Research

Transmission

Vaccination and Treatment

Epidemiology

Data Science and Modeling

Cardiovascular

Miscellaneous

COVID-19 Research Projects

One-year in: COVID-19 research at the international level in CORD-19 data

Introduction

Literature review and hypotheses

Science during the COVID-19 pandemic