· Weekly forecast
4%–8%
COVID-19, coronavirus disease 2019; SEIR, susceptible-exposed-infectious-removed; AI, artificial intelligence; CI, confidence interval; LASSO, least absolute shrinkage and selection operator; AUC, area under the curve; XGBoost, eXtreme gradient boosting; LSTM, long short-term memory; ARIMA, autoregressive integrated moving average; RMSE, root mean square error; RMSLE, root mean square logarithmic error; LR, linear regression; MLP, multilayer perceptron; RF, random forest; RNN, recurrent neural network; MAPE, mean absolute percentage error; CC, confirmed case; DC, death case; GEP, genetic evolutionary programming; RC, reported case; GRU, gated recurrent unit; ICU, intensive care unit; PA, prophet algorithm; NIPA, network inference-based prediction algorithm; CRP, C-reactive protein; GOF, goodness of fit; SVM, support vector machine; GLEAM, global epidemic and mobility framework.
To identify the likelihood of future results based on historical data, predictive analytics uses data, statistical algorithms, and different techniques such as machine learning, autoregressive integrated moving average (ARIMA) models, SEIR models, and long short-term memory (LSTM) models. The present SLR also classified papers on the basis of the techniques used ( Table 5 ) [ 12 , 28 – 56 ]. The most commonly used techniques used in predictive modeling and analysis were as follows:
Classification of papers by the technique/tool used
No. | Study | Year | Country | Citation (January 2, 2021) | Model |
---|---|---|---|---|---|
1 | Yang et al. [ ] | 2020 | China | 467 | SEIR and AI model |
2 | Liang et al. [ ] | 2020 | China | 327 | Statistical software |
3 | Yan et al. [ ] | 2020 | China | 194 | Machine learning |
4 | Gong et al. [ ] | 2020 | China | 134 | Statistical analysis |
5 | Chatterjee et al. [ ] | 2020 | India | 131 | SEIR |
6 | Hu et al. [ ] | 2020 | China | 130 | Artificial intelligence |
7 | Tomar & Gupta [ ] | 2020 | India | 129 | LSTM |
8 | IHME COVID-19 Health Service Utilization Forecasting Team & Murray [ ] | 2020 | USA | 119 | Statistical model |
9 | Chimmula & Zhang [ ] | 2020 | Canada | 99 | LSTM |
10 | Pandey et al. [ ] | 2020 | India | 57 | Machine learning |
11 | Jehi et al. [ ] | 2020 | USA | 45 | Statistical analysis |
12 | Ardabili et al. [ ] | 2020 | Worldwide scenario | 41 | Machine learning |
13 | Sujath et al. [ ] | 2020 | India | 41 | Machine learning |
14 | Qi et al. [ ] | 2020 | Worldwide scenario | 41 | Machine learning |
15 | Ghosal et al. [ ] | 2020 | India | 39 | Regression model |
16 | Hoertel et al. [ ] | 2020 | France | 37 | Statistical analysis |
17 | Arora et al. [ ] | 2020 | India | 34 | LSTM, RNN |
18 | Salgotra et al. [ ] | 2020 | India | 34 | GEP model |
19 | Dutta & Bandyopadhyay [ ] | 2020 | India | 33 | LSTM, GRU |
20 | Zhao et al. [ ] | 2020 | China | 13 | Logistic regression |
21 | Hernandez-Matamoros et al. [ ] | 2020 | Chile | 11 | ARIMA |
22 | Alazab et al. [ ] | 2020 | Jordon | 9 | PA, ARIMA, LSTM |
23 | Parbat & Chakraborty [ ] | 2020 | India | 9 | Regression model |
24 | Zhao et al. [ ] | 2020 | China | 6 | Grey Verhulst |
25 | Achterberg et al. [ ] | 2020 | China | 2 | Network-based forecasting |
26 | Fernandez et al. [ ] | 2021 | UK | 2 | AI |
27 | Li et al. [ ] | 2020 | Worldwide scenario | 1 | GLEM |
28 | Siwiak et al. [ ] | 2020 | India | 1 | ARIMA |
29 | Bhandari et al. [ ] | 2020 | UK | - | Logistic regression |
30 | Muhammad et al. [ ] | 2021 | Mexico | - | Machine learning |
SEIR, susceptible-exposed-infectious-removed; AI, artificial intelligence; LSTM, long short-term memory; RNN, recurrent neural network; GEP, genetic evolutionary programming; GRU, gated recurrent unit; ARIMA, autoregressive integrated moving average; PA, prophet algorithm; GLEM, global epidemic and mobility.
Machine learning is a technique used in which computers evaluate a data set and learn from the insights they gather. An artificial neural network is simulated by the use of complex algorithms that allow machines to classify, interpret, and understand data, and then use the insights that have been obtained to solve problems or make predictions. Common examples of machine learning include classification models, forecasts, medical diagnosis, image processing, regression, chatbots, and recommendation engines. Machine learning is a different branch of programming and is known to be an emerging technology.
ARIMA models can be built in an array of software tools, including Python. These models are used in statistics and econometrics to measure events that happen over a span of time. ARIMA models predict future data in a series using past data. An ARIMA model can be constructed for any number series that display patterns and is not a random event series. For example, sales data from a footwear store would be an example of time series data because the data are collected over a period of time. One of the key characteristics is that the data are collected at constant, regular intervals [ 57 ].
SEIR models are commonly used for assessing infection data during the different phases of an infectious outbreak. SEIR models are among the most widely adopted mathematical frameworks to describe disease dynamics and forecast potential contagion scenarios. After an infectious disease outbreak, a SEIR model can be helpful in determining the efficacy of different interventions, such as lock-downs. These models are based on a series of complex ordinary differential equations that take into account the number of people who are sick, the pattern of people who recover over time after sickness, and the people who die [ 58 ].
LSTM models are a type of recurrent neural network (RNN) used to predict new infection numbers over time by processing and forecasting several issues related to time series. With repeating modules like an RNN, an LSTM model has a chain-like structure, except that instead of a single neural network layer as in RNNs, an LSTM model has 4 layers that communicate in a slightly different manner, each of which performs its own special network role. In an LSTM cell, each repeating module has a cell state. Through using various gates in the cell, the LSTM cell has the power to add or subtract information to the cell state. There are 3 gates for the standard LSTM cell that control the sum of data input or output to/from the cell state and protect the cell state.
Regression analysis is a method of quantitative research that is used in studies modeling and analyzing several variables, where a dependent variable and 1 or more independent variables are included in the relationship. In basic terms, regression analysis is a mathematical approach used to evaluate the existence of the relationship between a dependent variable and 1 or more independent variables [ 59 ]. The 2 most widely used regression analyses are: (a) Logistic regression: in logistic regression, an independent variable is used to estimate the dependent variable. (b) Support vector regression (SVR): SVR provides the flexibility to determine how much error is suitable in a model and to find an appropriate line (or hyperplane in higher dimensions) to match the results.
Global epidemic and mobility (GLEM) models are being used in a number of COVID-19 related studies and analyses. These models involve a stochastic computational framework that combines high-resolution demographic and mobility data across the globe to predict the epidemic distribution across the globe. The goal of the GLEM model is to optimize versatility in specifying the disease compartment model and configuring the simulation scenario. It allows the user to set a number of criteria, including compartment-specific features, transition values, and environmental effects [ 60 ].
This study identified the core literature on prediction models for COVID-19. The aim of this research was to review and analyze the articles in the literature related to prediction models for COVID-19. A prediction model is a method for predicting the future scenario based on present facts. This SLR was based on a manual search of 1,196 papers published from January to December 2020, out of which 30 documents were selected on the basis of inclusion and exclusion criteria. Our SLR was conducted to explore which prediction models are currently available, with the goals of identifying various methods used to develop different types of prediction models and to conduct an effectiveness or quality assessment of models, which helps in evaluating their accuracy.
Based on this review, it is critical for statistical methods to be extensively used to predict the spread of infection. The LSTM [ 35 ] approach was used to track COVID-19 cases and to help government officials and policymakers in preparedness, with a root mean square error (RMSE) of 45.72. An ARIMA [ 47 ] model was used to predict the spread of COVID-19 infection with an average RMSE 44.81, followed by machine learning, artificial intelligence, and hybrid models. Lastly, in a few of the studies, mathematical modeling and network-based forecasting were used. SEIR models are among the most widely adopted mathematical frameworks to describe disease dynamics and forecast potential contagion scenarios. This SLR provides detailed information about various COVID-19 prediction models that can be adopted by researchers. This information can be used by healthcare professionals and by local government bodies in order to make decisions for managing healthcare facilities accordingly.
Ethics Approval
Not applicable.
Conflicts of Interest
The authors have no conflicts of interest to declare.
Availability of Data
Data for literature review was taken from Google Scholar, Scopus, and Web of Science. All data generated or analysed during this study are included in this published article. For other data, these may be requested through the corresponding author.
Authors’ Contributions
Conception: all authors; Design: all authors; Supervision: RS, DRS; Literature review: SMS, NSK, PPM; Writing–original draft: all authors; Writing–review & editing: all authors.
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The Covid-19 pandemic has been a severe global crisis, impacting almost everyone. It is a viral infection that has spread widely, affecting people in various ways. As a virus , it continues to evolve, leading to new variants. The pandemic has changed many aspects of daily life , including education and the economy. Many have lost their lives, jobs, and loved ones.
In this challenging time , Vedantu offers valuable support with online learning resources, helping students continue their education despite disruptions. By providing accessible and effective learning tools, Vedantu plays an important role in supporting students through these difficult times, ensuring they remain on track with their studies. Here, students can find short paragraph writing on Covid 19 in different word limits .
Do You Know? |
drives. Mention key responses and their effects in a concise manner. |
Read the article to learn how to write a Paragraph Writing on Covid 19.
To write a paragraph on Covid-19, start by introducing what the pandemic is and its global impact. Explain that Covid-19 is a viral infection that has affected millions of people around the world. Describe how it changed daily life, such as by disrupting the economy, education, and personal routines. Include specific examples, like the shift to online learning and the increase in remote work . Mention how the pandemic led to new health measures, such as social distancing and vaccinations. Conclude by summarising the overall impact and highlighting the importance of understanding these changes for future reference. Keep your sentences short and straightforward to ensure clarity.
Covid-19, caused by the novel coronavirus, greatly impacted the world. It spread rapidly, leading to a global health crisis. To control the virus, many countries implemented lockdowns, travel restrictions, and social distancing measures. These actions affected daily life, with people losing jobs, facing financial hardships, and schools shifting to online learning. The pandemic also overwhelmed healthcare systems. Despite these challenges, the global effort to combat the virus, including vaccination drives and medical research, aimed to bring an end to the crisis. Understanding these points helps us understand the wide-reaching effects of Covid-19 on our lives.
The COVID-19 pandemic, caused by the SARS-CoV-2 virus, began in late 2019 and rapidly spread across the globe, becoming one of the most challenging public health crises in recent history. This virus has led to a wide range of health issues, from mild symptoms to severe illness, causing significant loss of life. In response, countries introduced lockdowns, social distancing guidelines, and mask mandates, profoundly changing daily life and impacting various sectors. Educational institutions shifted to online learning, and many businesses adopted remote work or faced closures, leading to widespread economic difficulties. However, the swift development and distribution of vaccines have been crucial in managing the spread of the virus, significantly reducing severe cases and fatalities. The pandemic has underscored the importance of following public health guidelines, staying updated on health information, and supporting each other during these trying times. By understanding the pandemic's effects on health, society, and the economy, we can better navigate current challenges and prepare for future health crises. To conclude, Paragraph Writing On Covid 19 In 150 Words understanding the impact of COVID-19 helps us appreciate the importance of staying informed and prepared for future challenges.
COVID-19, caused by the SARS-CoV-2 virus, emerged in late 2019 and quickly escalated into a global pandemic. This virus spreads primarily through respiratory droplets when an infected person coughs or sneezes. It can lead to symptoms ranging from mild, such as a sore throat and fever , to severe conditions, including pneumonia and respiratory failure, which can be fatal. The pandemic has profoundly impacted every aspect of daily life, prompting governments worldwide to implement measures such as lockdowns, travel restrictions, and social distancing. These interventions, while essential for controlling the spread of the virus, have led to significant changes in how people live and work. Many businesses and schools shut down, shifting to remote work and online learning as the new norm. The pandemic has highlighted the importance of adhering to public health practices like regular handwashing, wearing masks, and maintaining physical distance .
Vaccines, developed at unprecedented speed, have played a crucial role in mitigating the severity of the disease and reducing mortality rates. Despite these advances, the pandemic has exposed and often exacerbated existing health inequalities and underscored the need for global cooperation in health emergencies. By understanding the impacts of COVID-19, we can better appreciate the importance of preparedness and resilience in addressing future health crises. Adhering to health guidelines remains crucial for safeguarding ourselves and our communities.
COVID-19, caused by the coronavirus SARS-CoV-2, emerged in December 2019 in Wuhan, China. It quickly evolved into a global pandemic, significantly altering daily life across the world. The virus spreads mainly through respiratory droplets from coughing, sneezing, or talking, but it can also be transmitted by touching surfaces contaminated with the virus and then touching the face. Symptoms range from mild, such as cough and fever, to severe, including pneumonia and difficulty breathing. The pandemic triggered unprecedented global responses, including lockdowns, travel restrictions, and social distancing measures. These actions, aimed at limiting the virus's spread, caused widespread disruptions to economies and education systems. Many businesses faced closures, and educational institutions shifted to remote learning, highlighting the need for digital infrastructure and adaptability. Healthcare systems worldwide faced immense pressure, revealing both strengths and weaknesses in pandemic preparedness. The rapid development and distribution of vaccines have been crucial in reducing severe illness and deaths. However, challenges remain, such as ensuring equitable vaccine distribution, managing public health compliance, and addressing the economic fallout. COVID-19 has underscored the importance of global collaboration and timely health interventions. It has shown the need for robust healthcare systems, effective communication , and individual responsibility in combating health crises. The pandemic has also emphasized the significance of science and technology in addressing global challenges and the importance of being prepared for future health emergencies. Continued vigilance, effective health strategies, and community solidarity are essential for overcoming the pandemic and mitigating its long-term impacts. Additionally, COVID-19 has highlighted the resilience and adaptability of communities around the world in the face of unprecedented challenges.
Example 1: how covid-19 ended and vaccination’s role.
The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, significantly impacted global health and daily life since late 2019. To combat the virus, countries introduced lockdowns, social distancing, and travel restrictions. The end of the pandemic began with the development and distribution of effective vaccines, which reduced severe cases and deaths. Mass vaccination campaigns worldwide, combined with public health measures, helped control the virus's spread. As more people were vaccinated and natural immunity developed, the number of new cases declined. By late 2021 and into 2022, many countries started easing restrictions, although the virus continued to circulate in various forms. Global cooperation and adherence to health guidelines played crucial roles in bringing the pandemic under control, though it remains important to monitor and manage ongoing cases.
The COVID-19 pandemic affected everyone in profound ways. Individuals experienced disruptions in daily routines, with many facing job losses, financial difficulties, and isolation from loved ones due to lockdowns and social distancing. Schools shifted to online learning, creating challenges for students and parents alike. Healthcare systems were overwhelmed, with hospitals struggling to manage the surge in patients. Businesses faced closures and reduced operations, impacting economies globally. The pandemic also highlighted disparities in healthcare access and resources. Communities had to adapt to new ways of living, from wearing masks to changing work environments. Despite the difficulties, the pandemic showed the resilience of people worldwide and the importance of community support and public health measures in navigating such crises.
Here are some engaging tasks for students to help them learn how to write a Paragraph on Covid 19:
Task 1 : Describe the Impact of COVID-19 on Daily Life.
Task 2 : Write a Paragraph on How Different Countries Handled the Pandemic.
Task 1: describe the impact of covid-19 on daily life.
The COVID-19 pandemic drastically changed people's daily lives around the world. With lockdowns and social distancing measures, many people had to adapt to working from home. Schools shifted to online learning, which presented challenges for both students and teachers in maintaining engagement and managing resources. Social interactions were limited to virtual meetings and phone calls, reducing face-to-face contact with friends and family. Many people adopted new habits, such as wearing masks and using hand sanitiser regularly, to stay safe. The pandemic also highlighted the importance of health and hygiene , influencing daily routines in profound ways.
During the COVID-19 pandemic, different countries adopted various strategies to manage the crisis. For example, New Zealand implemented early and strict lockdown measures, along with comprehensive testing and contact tracing, which effectively controlled the spread of the virus and allowed for quicker economic reopening. In contrast, the United States initially struggled with inconsistent lockdowns and testing shortages, leading to a higher number of cases. While the U.S. eventually increased vaccine distribution, the delayed response in the early months contributed to a more prolonged impact on public health. This comparison shows that early intervention and consistent measures can significantly influence the outcome of pandemic management.
This page on Paragraph Writing about COVID-19 gives a clear guide on how to write about the pandemic. It explains how to create well-structured paragraphs, using examples and tasks to make learning easier. Students will learn how to describe the impact of COVID-19, including its effects on daily life and the global response. The page also shows how to include important details and write clearly about the topic. By following the tips and examples provided, students will be able to write effective paragraphs on COVID-19 and understand its broader effects.
1. What is the main focus of a paragraph writing on Covid 19?
The main focus is to describe the impact of the COVID-19 pandemic. This includes its effects on health, daily life, and the global response. Keep your details clear and relevant.
2. How should I start a paragraph about COVID-19?
Begin with a clear topic sentence that introduces the main point about COVID-19. This could be about its spread, impact, or measures taken. Make sure it's engaging and informative.
3. What details should be included in the body of the paragraph writing on Covid 19?
Include key facts like how COVID-19 spread, its effects on people’s lives, and the response from governments. Use simple, direct language and relevant examples to explain these points.
4. How do I keep the short paragraph writing on Covid 19 focused?
Stick to the topic by focusing on specific aspects of COVID-19, such as its impact on daily routines or health. Avoid adding unrelated information to keep the paragraph clear and relevant.
5. What is a good way to end a paragraph writing on Covid 19?
End with a concluding sentence that sums up the main points. You might reflect on the overall impact of COVID-19 or mention ongoing changes and future outlooks.
6. How can I make my paragraph writing on Covid 19 interesting?
Use engaging examples and personal anecdotes if relevant. Describe how COVID-19 has specifically affected different aspects of life to make the paragraph more relatable and interesting.
7. Should I use technical terms in my paragraph writing on Covid 19?
Avoid using too many technical terms. Instead, use simple language that anyone can understand. Explain any necessary terms briefly to ensure clarity.
8. How do I organise my short paragraph writing on Covid 19?
Start with an introduction sentence, follow with detailed sentences about COVID-19’s impact, and end with a concluding sentence. This structure helps in presenting information.
9. How can I add depth to my short paragraph writing on Covid 19?
Include various perspectives, such as how COVID-19 affected different groups of people. Providing specific examples and detailed explanations adds depth and richness to your writing.
10. What should I avoid in my paragraph writing on Covid 19?
Avoid including personal opinions or speculative information. Stick to factual, relevant details about COVID-19 to maintain objectivity and accuracy.
11. How can I check if my paragraph writing on Covid 19 is clear?
Read your paragraph out loud to see if it flows well. Ask someone else to read it and provide feedback on clarity and understanding. Make sure each sentence contributes to the main point.
12. Can I use quotes or statistics in my short paragraph writing on Covid 19?
Yes, using quotes or statistics can add credibility to your paragraph. Just make sure to explain them clearly and relate them directly to the main points about COVID-19.
COVID-19 spreads primarily from person to person in several different ways:
Last updated: 8 August 2023
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Scientific Reports volume 13 , Article number: 18761 ( 2023 ) Cite this article
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The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnosis on different prevalence scenarios. With the objective of improving and accelerating the diagnosis of COVID-19, a multi modal prediction algorithm (MultiCOVID) based on CXR and blood test was developed, to discriminate between COVID-19, Heart Failure and Non-COVID Pneumonia and healthy (Control) patients. This retrospective single-center study includes CXR and blood test obtained between January 2017 and May 2020. Multi modal prediction models were generated using opensource DL algorithms. Performance of the MultiCOVID algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar–Bowker test. A total of 8578 samples from 6123 patients (mean age 66 ± 18 years of standard deviation, 3523 men) were evaluated across datasets. For the entire test set, the overall accuracy of MultiCOVID was 84%, with a mean AUC of 0.92 (0.89–0.94). For 300 random test images, overall accuracy of MultiCOVID was significantly higher (69.6%) compared with individual radiologists (range, 43.7–58.7%) and the consensus of all five radiologists (59.3%, P < .001). Overall, we have developed a multimodal deep learning algorithm, MultiCOVID, that discriminates among COVID-19, heart failure, non-COVID pneumonia and healthy patients using both CXR and blood test with a significantly better performance than experienced thoracic radiologists.
Introduction.
The outbreak of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), stroke the worldwide population with more than 200 million cases and 4.5 million deaths by August 2021. The rapid spread of the pandemic led to a global overexertion of health care and research facilities in order to counteract the growing rate of infection. However, a collapse of the sanitary system was imminent and inevitable worldwide, and new technologies were needed to speed up the diagnostic process.
The reference for COVID-19 diagnosis is the detection of SARS-CoV-2 viral RNA by real-time polymerase chain reaction (RT-PCR). However, the massive requests for sample processing at the beginning of the pandemic caused serious delays to obtain results.
As lung involvement is one of the main causes of morbidity and mortality in SARS-CoV-2 infection, a quick identification of characteristic findings in chest imaging can support the diagnosis and speed up the identification of COVID-19 positive patients at the emergency units.
Several studies have shown that implementation of deep learning (DL) tools to detect chest X-rays (CXR) findings typically associated with SARS-CoV-2 infection, deliver comparable results to those acquired by interpretation of radiologists. However, most of the trained models have a drop in their prediction performance when tested over external datasets 1 . In addition, one of the main hurdles to overcome when training an algorithm to detect Sars-CoV-2 infection in CXR is the similarity of findings with other entities like bacterial pneumonias or heart failure 2 . On the other hand, models based on laboratory results of peripheral blood also give predictive results on diagnosis 3 and prognosis 4 .
A key fact to highlight is how the incursion of COVID-19 caused a dramatic drop in the emergency room consultations of other pathologies. Later on, after the initial peak, the decline of the COVID-19 prevalence made the non-COVID diseases emerge once again at the hospitals. This is relevant due to the challenge of performing an efficient differential diagnosis with selected pathologies during a pandemic. It is well known that the predictive value of a diagnostic test is conditioned by the prevalence of the disease and that of COVID varies widely throughout the different waves of the pandemic 5 . A multicategory approach that takes into account differential diagnoses that are more stable in their prevalence could reduce this variability.
With the objective of improving and accelerating the diagnosis of COVID-19, we developed a tool to assist physicians in reaching a diagnosis. This tool is a multi-modal prediction algorithm (MultiCOVID) based on CXR and blood test with the ability to discriminate between COVID-19, Heart Failure (HF), Non-COVID Pneumonia (NCP) and healthy (Control) samples.
We retrospectively collected CXR images and hemogram values from 8578 samples from 6123 patients and healthy subjects (mean age 66 ± 18 years of standard deviation, 3523 men) from Parc Salut Mar (PSMAR) Consortium, Barcelona, Spain. Four cohorts were designed: (i) 1171 samples from patients diagnosed with COVID-19 by RT-PCR from March to May 2020; (ii) 1008 samples of patients who suffered an episode of heart failure between 2012 to 2019; (iii) 490 samples of patients diagnosed with non-COVID pneumonia (NCP) from 2018 to 2019; (iv) 5909 samples of standard preoperatory studies of healthy subjects from 2017 to 2019 (Fig. 1 ). HR and NCP diagnosis were selected as defined by the International Classification of Diseases, Tenth Revision (ICD-10) code. All the CXR images from groups i-iii were validated by two independent radiologists (MB and JM).
Flowchart for sample selection and patient inclusion in the study and breakdown of training, validation, and hold-out test data sets. Around 25,000 entries were obtained using both CXR images and blood test in a time wise manner. The whole dataset totals 8822 entries of paired CXR and blood test data. Samples with low completeness (less than 80% of blood test data available) were discarded for the model building.
We included CXR images performed in a period ranging from 1 day before the patient’s diagnosis to 7 days after. The images were filtered to include only frontal projections regardless of the quality and the radiography system used. Blood sample results were collected within a range of 2 days before or 7 days after the CXR acquisition date using PSMAR lab record system, except for control samples whose measurements ranged for 2 weeks. If two or more blood test results were collected, measurements were averaged.
CXR images and blood test results were combined in the same dataset and split into train/validation set (90%), and hold-out test (10%) set. For training/validation split, we divided the dataset in training (80%) and validation (20%) sets with 5 different random seeds. We ensured that there were no cross-over patients between groups.
Detailed description of the models, training policy and image preprocessing are provided in Supplementary Material . In brief, segmentation model is based on a U-Net architecture 6 . The CXR-only classification model consists of a validated Convolutional neural network (CNN) resnet-34 architecture 7 . Tabular only-model is an Attention-based network (TabNet) 8 . Joint model is a multi-modal deep learning algorithm which merges the CXR-only and the Blood-only models and uses both CXR image and blood tests as input values. It uses Gradient Blending in order to prevent overfitting and improve generalization 9 . MultiCOVID model is an ensemble predictor of 5 different Joint models that would classify independently between the different classes. Then it uses majority vote to assign a final classification. The whole pipeline development and training was performed using fastai deep learning API 10 .
Hold-out test dataset consisting of 300 samples (ensuring no patient overlap with training or validation sets) was used for expert interpretation. Each sample consisted of a CXR with matched blood results. Expert interpretations were independently provided by five board-certified thoracic radiologists (FZ, SC, LdC, DR, AG) with 2–30 years post-residency training experience. Radiologists were able to check both non segmented images and blood test results without any other additional information in a platform created ad-hoc for prediction. They provided a classification for each image in one of the four categories (COVID-19, control, HF and NCP). A consensus interpretation for the radiologist was obtained by the majority vote for each paired CHX-blood test analyzed.
A two-tailed t-test P value was reported when clinical and population blood test differences were assessed. McNemar–Bowker test was used to compare model performance against radiologist majority vote using FDR correction. Plotting and statistical analyses were performed using the packages ggplot, ggpubr and rcompanion in R, version 3.6 (R Core Team; R Foundation for Statistical Computing).
The study was designed to use radiology images and associated clinical/demographic/ laboratory patient information already collected for the purpose of performing clinical COVID-19 research by Hospital del Mar. The study was conducted in accordance with the relevant institutional guidelines and regulations. The experimental protocols, data acquisition and analysis were approved by the Parc de Salut Mar Clinical Research Ethics Committee (2020/9199/I). Informed consent was obtained, when possible, from patients or legal representatives or waived by the local Parc de Salut Mar Clinical Research Ethics Committee (2020/9199/I) if informed consent was not available due to the pandemic situation.
A total of 8578 samples were evaluated across datasets. Patient characteristics and blood test parameters are shown in Table 1 . A highly significant difference in age was found between the cohort of patients with heart failure (82.8 ± 10 years) and the other three cohorts (66.0 ± 16 years for COVID-19 samples, 63.2 ± 18 years for control samples and 67.8 ± 17 years for NCP samples, P < 0.001 for each comparison) and was not considered as a valid variable for further classification.
Previous studies have demonstrated that deep learning (DL)-based algorithms should be rigorously evaluated due to their ability to learn non relevant features in order to increase its prediction accuracy 1 . For this reason, we first developed a segmentation algorithm able to segment lung parenchyma at a 95%-pixel accuracy. Then, after segmentation, we evaluated the accuracy of the algorithms for three complementary datasets: non-segmented images, segmented regions and excluded regions. After a few training epochs the three different models achieved nonrandom accuracies between 67 and 74% (Fig. 2 A). However, attention map exploration on the images showed that the different models based their predictions not only inside but also outside of the lung parenchyma (Fig. 2 B).
Performance of visual models on whole CXR images. ( A ) Confusion matrix and overall accuracy using whole image, segmented and inverse segmented images, respectively for each category tested. ( B ) Raw image and Grad-CAM heatmap representation of an image for each category and model trained.
These observations showed that, although there are important features outside the lung parenchyma that may help the model to classify between the different entities (eg. heart size), there are other elements (eg. oxygen nasal cannulas or intravenous (IV) catheters) that might confound the model. Thus, we decided to first segment all the CXR before training our models for prediction of diagnosis. In order to accomplish this task, we generated a 785-radiology level lung segmentation dataset and trained a U-net model to regenerate the whole CXR dataset keeping only the lung parenchyma.
In order to evaluate the prediction capacity of both segmented CXR and blood sample data, we built different DL models using both sources alone or in combination. Metrics comparison of all the single vision (CXR-only) and tabular (Blood-only) models are detailed in Supplementary Material . As expected, CXR-only models had a more robust prediction of all 4 categories tested compared to Blood-only models (Fig. 3 ). This difference is stronger in the classes with less samples (HF, and NCP) where CXR-only models could identify features in the CXR images which are characteristic of these two entities whereas this was not possible with Blood-only models.
Performance of different models on the entries from hold-out test datasets. Means for precision (green), sensitivity (blue), F1 score (yellow), AUC (red) and accuracy (black diamond) for each model type and category assessed, respectively. CXR-only models use only CXR images for 4 category classification. Blood-only models use blood test a source of information. Joint model uses both CXR and blood test as input for classification and MultiCOVID is the majority vote of 5 different Joint models.
Model interpretability of Blood-only models by analyzing feature importance using Shapley Additive explanations 12 showed that patient classification was related to two different axes: the immune compartment and the red blood cell (RBC) compartment, respectively (Fig. 4 A). The first axis seems to be strongly associated with COVID-19 classification and shows a specific signature looking at the blood counts (Fig. 4 B-top). However, the second axis seems to subdivide patients between COVID-19/Control and HF/NCP, although COVID-19 blood counts seems to be statistically different from Control samples, too (Fig. 4 B-bottom).
Blood-only model interpretability by SHAP analysis. ( A ) Summary plot showing the mean absolute SHAP value of the ten most important features for the four classes. ( B ) Blood test values of the different features identified by SHAP analysis. RDW-CV: red cell distribution width; MCHC: Mean Corpuscular Hemoglobin Concentration; RBC: red blood cells.
The combination of CXR and blood tests using multimodal models that combine inputs from tabular and image data to perform a global prediction, slightly increased the prediction capacity of the single models even when DL tabular models are worse than machine learning (ML—XGBoost) models alone (Supplementary Table 1 ). This underpins the concept that adding new sources of information to the data could increase the ability of the models to generate better predictions 13 . Moreover, the joint approach used for building MultiCOVID algorithm resulted on an improved performance in the majority of the metrics analyzed (Fig. 3 and Supplementary Table 1 ).
Finally, we compared the performance of MultiCOVID algorithm with the interpretation of expert chest radiologists. This comparison was performed with 300 CXR randomly selected from the hold-out test set that were independently reviewed by 5 radiologists together with the blood test results. The independent results from radiologists showed an accuracy ranging from 43.7 to 58.7%. This value rose to 59.3% (178/300) when the consensus interpretation of all 5 radiologists based on the majority vote was considered. Of note, the overall accuracy achieved by MultiCOVID was 69.6% (209/300) that was significantly higher than consensus interpretation ( P < 0.001). In addition, for COVID-19 prediction individually, MultiCOVID showed similar sensitivity to the radiologists’ consensus but with a much higher specificity, leading to significantly better performance when discerning between COVID-19 versus Control and COVID-19 vs HF patients ( P < 0.05 for both comparisons; Fig. 5 ).
Comparison of the performance of MultiCOVID model with consensus expert radiologist interpretations on random sample of 300 images from the test set. The receiver operating characteristic (ROC) curves for each category (COVID-19 – blue; Control – green; Heart Failure (HF) – red and Non-COVID Pneumonia (NCP) – magenta) are shown for MultiCOVID (DL) and for the consensus interpretation of radiologists (majority vote). Sensitivity (Sens) and specificity (Spec) are also plotted for each category assessed. DL: deep learning.
Diagnosis of COVID-19 is an evolving challenge. During the beginning of the pandemic and the successive peaks with high prevalence rates, a prompt and effective diagnosis was critical for proper patient isolation and evaluation. However, since the prevalence of the COVID-19 cases oscillated, showing fewer cases between waves, and more non-COVID cases, it was important to differentiate patients with other diseases than COVID-19 presenting similar visual characteristics in the CXR.
During patient assessment in the emergency room, clinicians take into account different inputs for a proper diagnosis. First, the anamnesis, symptoms, vitals and physical findings guide the physician to an initial assumption. Based on this information, additional tests are requested (CXR, blood test, ECG and SARS-CoV-2 detection). The integration of these results allows the team to diagnose a patient accurately. However, this process is time consuming and sometimes findings are difficult to interpret, leading to misdiagnosis.
To improve this diagnostic process, we have developed and trained a multimodal deep learning algorithm based in a multiple input approach combining CXR images together with blood sample data to identify COVID-19 diagnosis with high sensitivity. This way we were able to manage the increased complexity of the dataset. These data from multiple sources are somehow correlated and complementary to each other and could reflect patterns that are not present in single models alone 13 .
Hence, MultiCOVID is fed by two of the most common and fast clinical tests requested in the emergency room (CXR and Blood test) and can predict the presence of three different diseases (COVID-19, heart failure and non-COVID pneumonia) with similar CXR characteristics.
Analysis of single models shows the importance of model interpretation. While CXR-only models could identify patterns outside the lung parenchyma that could diminish its generalization capacity 9 , Blood-only models could point to interesting population of cells that are differently represented in COVID-19 patients, leveraging its prediction capacity. In this context, the immune compartment plays an important role in the COVID-19 response, and it has been already published that COVID-19 patients present fewer overall leukocytes counts and, more concretely, eosinophil counts 14 , 15 . Furthermore, oxygen transport seems to be somehow affected, modulating the red cell population. In this regard, in our work we found significant differences in the erythrocyte count and the hemoglobin concentration. Although most of the studies correlate the reduction of this values to severe COVID-19 patients 16 , this is the first dataset to compare them in these four different categories at the time of diagnosis.
Moreover, although a huge amount of literature about COVID-19 diagnosis and prognosis has been published using only blood tests 17 , 18 , 19 , 20 or CXR 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 this is the first study that combines both parameters and compares its prediction capacity at diagnosis. Of note, only one previously published study integrates both blood test and CXR severity scores in order to determine in-hospital death of COVID-19 patients 29 . Hence, it is clear that merging both sources of data leads to a better prediction performance when compared with the two single models alone and that this difference is more pronounced where the number of cases is scarce. It is important to stress that this combination of data sources addresses the variable prevalence of COVID-19 cases during the pandemic, which is an issue that could not be solved in previous studies 23 , 24 .
Our study has several limitations. First, the algorithm was evaluated on a single center; thus, there was likely some degree of bias. Additionally, the sample collection was performed in different time periods for each group of patients, which could present some kind of differences in the CXR image acquisition although this was partially solved using the lung segmentation model which removes the noise signal present outside the lung parenchyma. And finally, model performance could be influenced by potential shifts in the disease landscape due to COVID-19 variants and vaccination efforts, which could influence the generalizability and interpretation of our findings.
We have developed a multimodal deep learning algorithm, MultiCOVID, that discriminates among COVID-19, heart failure, non-COVID pneumonia and healthy patients using both CXR and blood test with a significantly better performance than experienced thoracic radiologists.
Our approach and results suggest an innovative scenario where COVID-19 prediction could be identified from other similar diseases and facilitate triage within the emergency room in a COVID-19 low prevalence situation.
Our code base is provided on GitHub at https://github.com/Tato14/MultiCOVID , including weights for each of the individually trained neural network architectures and respective model weights for the weighted ensemble model. The datasets used and analyzed during the current study will be available from the corresponding author on reasonable request. In order to correct samples bias 11 , additional metadata information present in the DICOM image headers from the CXR would be also available upon request.
Deep learning
Chest X-rays
Area under the receiver operating characteristic curve
Coronavirus disease 2019
Reverse-transcription polymerase chain reaction
Severe acute respiratory syndrome coronavirus 2
Heart failure
Non-COVID pneumonia
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