# At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

Munib Mesinovic, Xin Ci Wong, Giri Shan Rajahram, Barbara Wanjiru Citarella, Kalaiarasu Peariasamy, Frank van Someren Greve, Piero Olliaro, Laura Merson, Lei Clifton, Christiana Kartsonaki, et al.

► To cite this version:

Munib Mesinovic, Xin Ci Wong, Giri Shan Rajahram, Barbara Wanjiru Citarella, Kalaiarasu Peari- asamy, et al. At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods. Scientific Reports, 2024, 14 (1), pp.16387. 10.1038/s41598-024-63212-7. hal-04831133v2

HAL Id: hal-04831133

https://hal.science/hal-04831133v2

Submitted on 10 Jun 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
<figure><img src="image_2.png" /></figure>

OPEN

# At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

Munib Mesinovic<fnref n="1" />,², Xin Ci Wong², Giri Shan Rajahram³, Barbara Wanjiru Citarella⁴, Kalaiarasu M. Peariasamy², Frank van Someren Greve⁵, Piero Olliaro⁴, Laura Merson⁴, Lei Clifton⁶, Christiana Kartsonaki⁶ & ISARIC Characterisation Group*

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients.

## Clinical background

On the last day of 2019, the WHO received information about 44 cases of pneumonia-like disease in Wuhan city, China<fnref n="1" />. By 5 September 2022, more than 600 million cases of SARS-CoV-2 infection had been reported across all continents, regions, and most countries, resulting in nearly 6.5 million deaths².

<footnote n="1">¹Department of Engineering Science, University of Oxford, Oxford, UK. ²Digital Health Research and Innovation Unit, Institute for Clinical Research, National Institutes of Health (NIH), Shah Alam, Malaysia. ³Queen Elizabeth II Hospital, Ministry of Health, Kota Kinabalu, Malaysia. ⁴Pandemic Sciences Institute, ISARIC, University of Oxford, Oxford, UK. ⁵Department of Medical Microbiology, Amsterdam University Medical Center, Amsterdam, The Netherlands. ⁶Nuffield Department of Population Health, University of Oxford, Oxford, UK. *A list of authors and their affiliations appears at the end of the paper. †email: munib.mesinovic@jesus.ox.ac.uk</footnote>
COVID-19, the disease caused by infection with SARS-CoV-2, has a high mortality rate in hospitalised patients with deaths predominantly caused by respiratory failure³. It continues to this day to be a challenging global pandemic with significant morbidity and mortality⁴. As Knight et al.⁵ indicate, prognostic models that can predict outcomes among COVID-19 patients can be used to support clinical decision-making regarding hospital treatment and prioritisation. One such score is the 4C score that includes data about patient comorbidity, abnormal physiology, and inflammation using routinely measured data, bedside observations, and biochemistry tests⁶. While in most cases COVID-19 is a mild illness, those at highest risk of death and severe complications usually are hospitalised some time after onset⁷.

Pulmonary embolism (PE) is among the most severe and preventable complications of COVID-19 characterised by increased D-dimer levels and high thrombosis risk that has been repeatedly reported across different countries⁸. Studies suggest PE incidence rates above 15% in the ICU for COVID-19 patients and early recognition of its risk factors can help in identifying urgent treatment with anticoagulation therapy to those most in clinical need⁴,⁹. Recent international studies additionally suggest COVID-19 as a key risk factor for pulmonary embolism both in the short- and long-term⁹,¹⁰. Existing PE prediction models are limited in part because they were developed for non-COVID-19 patients and traditional risk factors for PE may not be as predictive. If risk models can be developed for assessing occurrence of PE in COVID-19 patients across different countries, that can be an important step forward in preventing this serious complication of COVID-19, especially given the current epidemiological situation⁹.

As for risk factors that contribute the most to the occurrence of mortality and pulmonary embolism in COVID-19 patients, age has been established as the dominant predictor of mortality¹¹. Furthermore, studies have described other risk factors of COVID mortality such as cardiovascular disease, chronic respiratory disease, diabetes, hypertension, smoking, and obesity¹².

## Technical background

Machine learning has been applied to different COVID-19 related questions. Large amounts of patient data are being generated during the COVID-19 pandemic which can be useful for predictive modelling. Using machine learning with large amounts of complex patient data could generate accurate and patient-specific predictions and assist clinicians.

Previous research includes¹³ exploring in-hospital mortality with logistic regression on just 191 patients and¹⁴ have followed with multi-center validation with 299 patients for internal training and 145 patients for external validation.¹⁵ have looked at regression-based predictions of all-cause mortality with hospital admission time as a predictor and using hazard models yet their results have also been limited due to a smaller dataset restricting generalisability. All of these studies have used a combination of demographics, comorbidities, symptoms, laboratory tests, and self-reported onset times.

In this study, we investigated how pulmonary embolism and all-cause mortality vary across subgroups of a large and international cohort. We also show how predictive certain clinical factors gathered from patients with COVID-19 can be to the respective outcomes. In studies looking at predicting thromboembolism more broadly, a defining limitation for impactful and generalisable application of machine learning methods has been a small patient sample and a lack of systematic comparison of algorithms¹⁶. Applying a diverse set of methods to one of the largest and most diverse datasets on hospitalised patients with COVID-19 can help find the best mechanism for risk prioritisation of patients in a timely way and may help reduce mortality and risk of PE in those with COVID-19.

## Results

Variable distributions can be seen in Tables 1, 2, 3, and 4. A detailed collection of figures for variable distribution across age groups can be found in the Supplementary.

Several variables were highly correlated with PE and death (Supplementary Figures 3 and 4, Tables II and III). Multivariable logistic regression shows high association of country, age, alpha variant, and certain symptoms with PE and death (Figs. 1 and 2). Tables with *p*-values are included in Tables 5, 6, and 7.

The Cox proportional hazards model without regularisation yielded a C-index of 0.71 and the forest plot shows high hazard ratios for age, certain regions of admission, and specific symptoms (Fig. 3 and Tables 8 and 9).

The Kaplan-Meier curves for risk stratification across age, sex, and region groups show clear difference in risk with older men and those in South Asia and the Middle East with the lowest rates of survival (Fig. 4).

Tables 10, 11, and 12 show superior performance of the XGBoost model across all 3 test sets. Similarly, XGBoost maintains sensitive and accurate prediction of death compared to other alternative models (Table 13). The validation scores are for the combined UK and Spain set.

The model also maintains high predictive performance across various subgroups of the patient population stratified across sex and age (Tables 14 and 15).

To further evaluate our model, we test it on held-out test data with specific patient population subgroups including men, women, and different age groups as can be seen in Tables 14 and 15. Our model shows reliable prediction for PE and mortality in both men and women without a significant difference in performance for each group, whereas for age groups there is greater variation in results as compared to sex differences but it remains relatively consistent in predictive performance.

Taking the best performing XGBoost model and applying 2 different feature importance methods, average f1-score gain across splits and Shapley values, we obtain the results seen in Figs. 5, 6, 7, and 8. A feature importance stratification on a held-out test set of only men and only women separately for either PE or mortality prediction is also included in Figs. 9, 10, 11, and 12. As further clarification for the SHAP plot, darker colour indicates that a higher value of that feature contributes to the prediction either positively (if on the right hand
<table><caption>**Table 1.** Baseline characteristics stratified by occurrence of PE (median and IQR used for lab measurements). PE here also includes positive cases of deep vein thrombosis and thromboembolism.</caption><thead><tr><th rowspan="2">Characteristic</th><th colspan="2">PE (N = 5656)</th><th colspan="2">Non-PE</th></tr><tr><th>Missing (%)</th><th>Mean (SD)/count (%)</th><th>Missing (%)</th><th>Mean (SD)/count (%)</th></tr></thead><tbody><tr><td>Age</td><td>0.8</td><td>62.6 (15.6)</td><td>4.1</td><td>56.4 (20.9)</td></tr><tr><td>Sex (male)</td><td>0.1</td><td>3733 (66.0)</td><td>2.1</td><td>385,038 (48.4)</td></tr><tr><td>Alpha variant (post)</td><td>-</td><td>3,733 (66.0)</td><td>2.1</td><td>385,038 (48.4)</td></tr><tr><td>Ethnicity</td><td></td><td></td><td></td><td></td></tr><tr><td>White</td><td>-</td><td>309 (5.5)</td><td>-</td><td>12,030 (1.5)</td></tr><tr><td>South Asian</td><td>-</td><td>32 (0.6)</td><td>-</td><td>9224 (1.2)</td></tr><tr><td>Malay</td><td>-</td><td>0 (0.0)</td><td>-</td><td>3,812 (0.5)</td></tr><tr><td>Latin American</td><td>-</td><td>24 (0.4)</td><td>-</td><td>2719 (0.3)</td></tr><tr><td>Other</td><td>-</td><td>5171 (91.4)</td><td>-</td><td>757,656 (95.3)</td></tr><tr><td>Country</td><td></td><td></td><td></td><td></td></tr><tr><td>South Africa</td><td>-</td><td>0 (0.0)</td><td>-</td><td>432,596 (54.4)</td></tr><tr><td>United Kingdom</td><td>-</td><td>4076 (72.1)</td><td>-</td><td>269,073 (33.9)</td></tr><tr><td>Spain</td><td>-</td><td>577 (10.2)</td><td>-</td><td>14,764 (1.9)</td></tr><tr><td>Norway</td><td>-</td><td>15 (0.3)</td><td>-</td><td>7448 (0.9)</td></tr><tr><td>Other</td><td>-</td><td>988 (17.4)</td><td>-</td><td>71,853 (9.0)</td></tr><tr><td>Country income</td><td></td><td></td><td></td><td></td></tr><tr><td>High</td><td>-</td><td>5513 (97.5)</td><td>-</td><td>323,889 (40.8)</td></tr><tr><td>Upper middle</td><td>-</td><td>72 (1.3)</td><td>-</td><td>449,782 (56.6)</td></tr><tr><td>Lower middle</td><td>-</td><td>71 (1.2)</td><td>-</td><td>20,210 (2.5)</td></tr><tr><td>Low</td><td>-</td><td>0 (0.0)</td><td>-</td><td>918 (0.1)</td></tr><tr><td>Region</td><td></td><td></td><td></td><td></td></tr><tr><td>Sub-saharan Africa</td><td>-</td><td>0 (0.0)</td><td>-</td><td>433,522 (54.5)</td></tr><tr><td>Europe and Central Asia</td><td>-</td><td>5,264 (93.1)</td><td>-</td><td>314,416 (39.6)</td></tr><tr><td>South Asia</td><td>-</td><td>49 (0.9)</td><td>-</td><td>17,413 (2.2)</td></tr><tr><td>East Asia</td><td>-</td><td>39 (0.7)</td><td>-</td><td>10,421 (1.3)</td></tr><tr><td>North America</td><td>-</td><td>195 (3.4)</td><td>-</td><td>9,666 (1.2)</td></tr><tr><td>Other</td><td>-</td><td>107 (1.9)</td><td>-</td><td>9,498 (1.2)</td></tr><tr><td>Comorbidities</td><td></td><td></td><td></td><td></td></tr><tr><td>AIDS/HIV</td><td>15.5</td><td>19 (0.3)</td><td>28.3</td><td>27,895 (3.5)</td></tr><tr><td>Asthma</td><td>10.6</td><td>644 (11.4)</td><td>26.4</td><td>51,714 (6.5)</td></tr><tr><td>Chronic cardiac disease</td><td>8.3</td><td>1002 (17.7)</td><td>26.2</td><td>80,348 (10.1)</td></tr><tr><td>Chronic haematological</td><td>11.2</td><td>171 (3.0)</td><td>64.7</td><td>10,275 (1.3)</td></tr><tr><td>Chronic kidney disease</td><td>8.8</td><td>516 (9.1)</td><td>27.0</td><td>46,054 (5.8)</td></tr><tr><td>Chronic neurological</td><td>9.6</td><td>369 (6.5)</td><td>63.8</td><td>28,648 (3.6)</td></tr><tr><td>Chronic pulmonary</td><td>7.9</td><td>707 (12.5)</td><td>26.5</td><td>52,006 (6.5)</td></tr><tr><td>Dementia</td><td>9.8</td><td>214 (3.8)</td><td>64.5</td><td>27,374 (3.4)</td></tr><tr><td>Diabetes</td><td>10.4</td><td>1196 (21.1)</td><td>24.5</td><td>152,728 (19.2)</td></tr><tr><td>Hypertension</td><td>10.3</td><td>2219 (39.2)</td><td>27.1</td><td>226,285 (28.5)</td></tr><tr><td>Liver disease</td><td>7.3</td><td>169 (3.0)</td><td>62.3</td><td>8960 (1.1)</td></tr><tr><td>Malignant neoplasm</td><td>8.7</td><td>508 (9.0)</td><td>27.1</td><td>27,811 (3.5)</td></tr><tr><td>Malnutrition</td><td>15.1</td><td>59 (1.0)</td><td>67.3</td><td>5,582 (0.7)</td></tr><tr><td>Obesity</td><td>16.2</td><td>1,167 (20.6)</td><td>57.0</td><td>53,227 (6.7)</td></tr><tr><td>Rheumatologic</td><td>9.9</td><td>485 (8.6)</td><td>64.9</td><td>27,954 (3.5)</td></tr><tr><td>Smoking</td><td>45.5</td><td>1317 (23.3)</td><td>71.5</td><td>74,205 (9.3)</td></tr></tbody></table>

side of the vertical line) or negatively (if on the left hand side of the vertical line). Higher placement of the feature vertically in the plot means it has a higher mean Shapley value and hence contributes more to correct predictions in the model.

Discussion

To our knowledge, this multi-center dataset is the largest international cohort of hospitalised COVID-19 patients available. Our analysis showed that patients with PE are older, more often male, white, from higher income countries, and are more likely to suffer from: asthma, chronic cardiac disease, chronic kidney disease, chronic
<table><caption>**Table 2.** Baseline characteristics stratified by occurrence of PE (continued). PE here also includes positive cases of deep vein thrombosis and thromboembolism.</caption><thead><tr><th rowspan="2">Characteristic</th><th colspan="2">PE (N = 5656)</th><th colspan="2">Non-PE</th></tr><tr><th>Missing (%)</th><th>Mean (SD)/count (%)</th><th>Missing (%)</th><th>Mean (SD)/count (%)</th></tr></thead><tbody><tr><td>Symptoms</td><td></td><td></td><td></td><td></td></tr><tr><td>Symptomatic</td><td>2.3</td><td>5400 (95.5)</td><td>63.4</td><td>276,645 (34.8)</td></tr><tr><td>Abdominal pain</td><td>19.1</td><td>310 (5.5)</td><td>70.6</td><td>21,510 (2.7)</td></tr><tr><td>Confusion</td><td>14.4</td><td>624 (11.0)</td><td>70.5</td><td>45,559 (5.7)</td></tr><tr><td>Bleeding</td><td>19.0</td><td>95 (1.7)</td><td>70.8</td><td>4307 (0.5)</td></tr><tr><td>Chest pain</td><td>17.0</td><td>1,058 (18.7)</td><td>70.4</td><td>32,811 (4.1)</td></tr><tr><td>Conjunctivitis</td><td>23.2</td><td>15 (0.3)</td><td>72.0</td><td>1012 (0.1)</td></tr><tr><td>Cough</td><td>10.7</td><td>3434 (60.7)</td><td>67.7</td><td>153,548 (19.3)</td></tr><tr><td>Diarrhoea</td><td>14.1</td><td>886 (15.7)</td><td>69.9</td><td>39,718 (5.0)</td></tr><tr><td>Ear pain</td><td>43.4</td><td>7 (0.1)</td><td>77.0</td><td>765 (0.1)</td></tr><tr><td>Fatigue</td><td>18.5</td><td>2134 (37.7)</td><td>70.9</td><td>93,637 (11.8)</td></tr><tr><td>Headache</td><td>20.0</td><td>510 (9.0)</td><td>71.8</td><td>26,815 (3.4)</td></tr><tr><td>Fever</td><td>10.6</td><td>3,029 (53.6)</td><td>67.8</td><td>146,248 (18.4)</td></tr><tr><td>Lost sense of smell</td><td>24.7</td><td>403 (7.1)</td><td>77.4</td><td>12,736 (1.6)</td></tr><tr><td>Lost sense of taste</td><td>28.3</td><td>438 (7.7)</td><td>77.9</td><td>14,767 (1.9)</td></tr><tr><td>Lymphadenopathy</td><td>23.7</td><td>33 (0.6)</td><td>72.9</td><td>1264 (0.2)</td></tr><tr><td>Muscle/joint pain</td><td>20.5</td><td>903 (16.0)</td><td>71.8</td><td>40,627 (5.1)</td></tr><tr><td>Runny nose</td><td>26.0</td><td>111 (2.0)</td><td>73.0</td><td>8,244 (1.0)</td></tr><tr><td>Seizures</td><td>18.1</td><td>19 (0.3)</td><td>71.3</td><td>2764 (0.3)</td></tr><tr><td>Severe dehydration</td><td>64.9</td><td>245 (4.3)</td><td>85.3</td><td>13,732 (1.7)</td></tr><tr><td>Shortness of breath</td><td>6.8</td><td>4205 (74.3)</td><td>67.5</td><td>156,078 (19.6)</td></tr><tr><td>Skin rash</td><td>21.5</td><td>76 (1.3)</td><td>71.5</td><td>5,771 (0.7)</td></tr><tr><td>Sore throat</td><td>25.8</td><td>225 (4.0)</td><td>72.9</td><td>16,500 (2.1)</td></tr><tr><td>Vomiting</td><td>17.3</td><td>736 (13.0)</td><td>69.9</td><td>43,149 (5.4)</td></tr><tr><td>Wheezing</td><td>22.7</td><td>278 (4.9)</td><td>71.7</td><td>14,474 (1.8)</td></tr><tr><td>Lab measurements</td><td></td><td></td><td></td><td></td></tr><tr><td>D-dimer (µg/mL)</td><td>92.0</td><td>1.1 (0.5, 2.5)</td><td>98.6</td><td>0.7 (0.4, 1.3)</td></tr><tr><td>ALT (IU/L)</td><td>64.1</td><td>36.0 (22.0, 60.0)</td><td>86.0</td><td>27.0 (17.0, 45.0)</td></tr><tr><td>Bilirubin (µmol/L)</td><td>70.5</td><td>11.0 (8.0, 15.6)</td><td>85.7</td><td>9.0 (7.0, 14.0)</td></tr><tr><td>CRP (mg/L)</td><td>35.7</td><td>115.0 (59.0, 191.8)</td><td>79.7</td><td>74.9 (29.0, 143.0)</td></tr><tr><td>Lymphocytes (10<sup>3</sup>µL)</td><td>33.2</td><td>0.9 (0.6, 1.3)</td><td>78.4</td><td>0.9 (0.6, 1.3)</td></tr><tr><td>Neutrophils (10<sup>9</sup>L)</td><td>33.2</td><td>7.1 (4.8, 10.1)</td><td>78.4</td><td>5.5 (3.8, 8.2)</td></tr><tr><td>Platelets (10<sup>9</sup>L)</td><td>57.7</td><td>13.0 (11.5, 14.7)</td><td>88.0</td><td>12.8 (11.2, 14.3)</td></tr><tr><td>Blood Urea Nitrogen (mmol/L)</td><td>44.1</td><td>6.5 (4.7, 9.7)</td><td>79.8</td><td>6.4 (4.5, 10.0)</td></tr><tr><td>White Blood Cells (10<sup>9</sup>L)</td><td>30.0</td><td>8.8 (6.2, 12.1)</td><td>77.0</td><td>7.2 (5.3, 10.1)</td></tr><tr><td>Vital signs</td><td></td><td></td><td></td><td></td></tr><tr><td>Diastolic BP (mmHg)</td><td>6.9</td><td>75.5 (14.8)</td><td>64.7</td><td>74.8 (15.2)</td></tr><tr><td>Systolic BP (mmHg)</td><td>6.8</td><td>130.2 (23.2)</td><td>64.7</td><td>130.3 (24.5)</td></tr><tr><td>Heart rate (bpm)</td><td>7.4</td><td>96.3 (21.1)</td><td>65.4</td><td>92.0 (21.5)</td></tr><tr><td>Oxygen saturation (%)</td><td>6.8</td><td>90.7 (11.3)</td><td>64.7</td><td>93.4 (9.3)</td></tr><tr><td>Respiratory rate (brpm)</td><td>10.1</td><td>25.1 (8.0)</td><td>65.7</td><td>22.8 (7.0)</td></tr><tr><td>Temperature (°C)</td><td>7.1</td><td>37.2 (1.1)</td><td>64.2</td><td>37.2 (1.0)</td></tr><tr><td>Outcome</td><td></td><td></td><td></td><td></td></tr><tr><td>Discharge</td><td>-</td><td>3,492 (61.7)</td><td>-</td><td>519,423 (65.4)</td></tr><tr><td>Death</td><td>-</td><td>1297 (22.9)</td><td>-</td><td>162,091 (20.4)</td></tr><tr><td>Other</td><td>-</td><td>531 (15.4)</td><td>-</td><td>54,930 (14.2)</td></tr></tbody></table>

neurological disease, chronic pulmonary disease, hypertension, cancer, obesity, rheumatologic conditions, or smoke.

The occurrence of pulmonary embolism in our study population was 0.7% and our results showed a significant association between confirmed PE and mortality when compared with patients without PE as has been similarly found in patients without COVID-19¹⁷.
<table><caption>**Table 3.** Baseline characteristics stratified by death (median and IQR used for lab measurements).</caption><thead><tr><th rowspan="2">Characteristic</th><th colspan="2">Death (N = 163,388)</th><th colspan="2">No death</th></tr><tr><th>Missing (%)</th><th>Mean (SD)/count (%)</th><th>Missing (%)</th><th>Mean (SD)/count (%)</th></tr></thead><tbody><tr><td>Age</td><td>0.8</td><td>67.5 (16.1)</td><td>2.8</td><td>53.2 (21.0)</td></tr><tr><td>Sex (male)</td><td>0.2</td><td>87,679 (53.7)</td><td>0.5</td><td>282,993 (48.0)</td></tr><tr><td>Country</td><td></td><td></td><td></td><td></td></tr><tr><td>South Africa</td><td>-</td><td>96,965 (59.3)</td><td>-</td><td>332,012 (56.3)</td></tr><tr><td>United Kingdom</td><td>-</td><td>54,540 (33.4)</td><td>-</td><td>193,780 (32.9)</td></tr><tr><td>Spain</td><td>-</td><td>123 (0.1)</td><td>-</td><td>7486 (1.3)</td></tr><tr><td>Norway</td><td>-</td><td>41 (&lt; 0.1)</td><td>-</td><td>7216 (1.2)</td></tr><tr><td>Other</td><td>-</td><td>11,719 (7.2)</td><td>-</td><td>48,992 (8.3)</td></tr><tr><td>Country Income</td><td></td><td></td><td></td><td></td></tr><tr><td>High</td><td>-</td><td>59,531 (36.4)</td><td>-</td><td>231,101 (39.2)</td></tr><tr><td>Upper Middle</td><td>-</td><td>98,064 (60.0)</td><td>-</td><td>345,013 (58.5)</td></tr><tr><td>Lower Middle</td><td>-</td><td>5725 (3.5)</td><td>-</td><td>12,654 (2.1)</td></tr><tr><td>Low</td><td>-</td><td>68 (&lt;0.1)</td><td>-</td><td>717 (0.1)</td></tr><tr><td>Region</td><td></td><td></td><td></td><td></td></tr><tr><td>Sub-saharan Africa</td><td>-</td><td>97,033 (59.4)</td><td>-</td><td>332,730 (56.4)</td></tr><tr><td>Europe and Central Asia</td><td>-</td><td>57,129 (35.0)</td><td>-</td><td>222,268 (37.7)</td></tr><tr><td>South Asia</td><td>-</td><td>5232 (3.2)</td><td>-</td><td>11,491 (1.9)</td></tr><tr><td>East Asia</td><td>-</td><td>678 (0.4)</td><td>-</td><td>8,322 (1.4)</td></tr><tr><td>North America</td><td>-</td><td>2099 (1.3)</td><td>-</td><td>6776 (1.1)</td></tr><tr><td>Other</td><td>-</td><td>1237 (0.8)</td><td>-</td><td>7999 (1.4)</td></tr><tr><td>Comorbidities</td><td></td><td></td><td></td><td></td></tr><tr><td>AIDS/HIV</td><td>27.3</td><td>6,203 (3.8)</td><td>26.2</td><td>20,828 (3.5)</td></tr><tr><td>Asthma</td><td>25.2</td><td>10,425 (6.4)</td><td>24.6</td><td>40,402 (6.9)</td></tr><tr><td>Chronic Cardiac Disease</td><td>24.4</td><td>26,400 (16.2)</td><td>24.6</td><td>52,337 (8.9)</td></tr><tr><td>Chronic Haematological</td><td>65.1</td><td>2,901 (1.8)</td><td>65.8</td><td>7,095 (1.2)</td></tr><tr><td>Chronic Kidney Disease</td><td>25.6</td><td>16,693 (10.2)</td><td>25.3</td><td>28,559 (4.8)</td></tr><tr><td>Chronic Neurological</td><td>64.1</td><td>8,071 (4.9)</td><td>64.7</td><td>19,981 (3.4)</td></tr><tr><td>Chronic Pulmonary</td><td>25.0</td><td>16,224 (9.9)</td><td>24.7</td><td>34,869 (5.9)</td></tr><tr><td>Dementia</td><td>65.1</td><td>10,261 (6.3)</td><td>65.5</td><td>16,740 (2.8)</td></tr><tr><td>Diabetes</td><td>21.3</td><td>45,593 (27.9)</td><td>23.1</td><td>104,810 (17.8)</td></tr><tr><td>Hypertension</td><td>24.7</td><td>64,098 (39.2)</td><td>25.4</td><td>157,991 (26.8)</td></tr><tr><td>Immunosuppression</td><td>79.6</td><td>1364 (0.8)</td><td>81.9</td><td>3728 (0.6)</td></tr><tr><td>Liver Disease</td><td>62.4</td><td>2262 (1.4)</td><td>63.3</td><td>6269 (1.1)</td></tr><tr><td>Malignant Neoplasm</td><td>25.3</td><td>9036 (5.5)</td><td>25.5</td><td>17,719 (3.0)</td></tr><tr><td>Malnutrition</td><td>67.0</td><td>1717 (1.1)</td><td>67.1</td><td>3692 (0.6)</td></tr><tr><td>Obesity</td><td>55.1</td><td>11,176 (6.8)</td><td>56.3</td><td>39,153 (6.6)</td></tr><tr><td>Rheumatologic</td><td>65.2</td><td>7333 (4.5)</td><td>65.9</td><td>20,359 (3.5)</td></tr><tr><td>Smoking</td><td>72.5</td><td>17,655 (10.8)</td><td>71.5</td><td>53,764 (9.1)</td></tr><tr><td>Tuberculosis</td><td>55.5</td><td>2725 (1.7)</td><td>55.6</td><td>8688 (1.5)</td></tr></tbody></table>

Accordingly, our logistic regression models for PE and death showed that different age-groups experience different risks of either outcome. The age group 40-80 was at highest odds of having PE, and those >60 of dying as can be seen in the Kaplan-Meier curve in Fig. 4a. Symptomatic COVID-19 patients were almost 3 times more likely to experience PE while also being more likely to die. Within symptoms and comorbidities, shortness of breath, chest pain, obesity, and bleeding were associated with higher odds of a PE, followed by hypertension and loss of smell. The regionality of the data must be addressed in the higher odds of death in South Asia, Middle East and North Africa (MENA), and South Africa compared to Europe and Central Asia as the hospital centers in those communities have different challenges and circumstances when it comes to fighting the pandemic. Symptoms like shortness of breath, confusion, severe dehydration, and wheezing were present in COVID-19 patients with higher odds of death, and comorbidities such as malignant neoplasm, diabetes, and chronic kidney or liver disease also lead to higher risk of death. For both correlation and odds of PE and death, men were more at risk. This is shown in the Kaplan-Meier curves for survival stratified across sex in Fig. 4b.

The hazard ratios confirmed those over the age of 60 were at highest risk of death, especially those COVID-19 patients who experienced shortness of breath, severe dehydration, confusion, and had pre-existing chronic conditions. Regionality of hospital admission was once again an important risk factor for death. Interestingly,
<table><caption>**Table 4.** Baseline characteristics stratified by death (continued). PE here also includes positive cases of deep vein thrombosis and thromboembolism.</caption><thead><tr><th rowspan="2">Characteristic</th><th colspan="2">Death (N = 163,388)</th><th colspan="2">No death</th></tr><tr><th>Missing (%)</th><th>Mean (SD)/count (%)</th><th>Missing (%)</th><th>Mean (SD)/count (%)</th></tr></thead><tbody><tr><td>Symptoms</td><td></td><td></td><td></td><td></td></tr><tr><td>Symptomatic</td><td>63.9</td><td>57,578 (35.2)</td><td>61.7</td><td>211,379 (35.9)</td></tr><tr><td>Abdominal Pain</td><td>71.4</td><td>3,452 (2.1)</td><td>69.1</td><td>17,537 (3.0)</td></tr><tr><td>Confusion</td><td>69.7</td><td>16,267 (10.0)</td><td>69.3</td><td>29,022 (4.9)</td></tr><tr><td>Bleeding</td><td>71.3</td><td>1099 (0.7)</td><td>69.3</td><td>3202 (0.5)</td></tr><tr><td>Chest Pain</td><td>71.4</td><td>4554 (2.8)</td><td>68.7</td><td>27,581 (4.7)</td></tr><tr><td>Conjunctivitis</td><td>72.8</td><td>220 (0.1)</td><td>70.6</td><td>792 (0.1)</td></tr><tr><td>Cough</td><td>68.2</td><td>31,110 (19.0)</td><td>66.0</td><td>117,745 (20.0)</td></tr><tr><td>Diarrhoea</td><td>70.5</td><td>6860 (4.2)</td><td>68.3</td><td>31,776 (5.4)</td></tr><tr><td>Ear Pain</td><td>76.7</td><td>100 (0.1)</td><td>75.4</td><td>629 (0.1)</td></tr><tr><td>Fatigue</td><td>71.7</td><td>19,510 (11.9)</td><td>69.4</td><td>70,064 (11.9)</td></tr><tr><td>Headache</td><td>73.4</td><td>2469 (1.5)</td><td>70.1</td><td>22,228 (3.8)</td></tr><tr><td>Fever</td><td>68.0</td><td>29,252 (17.9)</td><td>66.3</td><td>111,358 (18.9)</td></tr><tr><td>Lost Sense of Smell</td><td>79.6</td><td>1131 (0.7)</td><td>75.6</td><td>10,998 (1.9)</td></tr><tr><td>Lost Sense of Taste</td><td>80.2</td><td>1671 (1.0)</td><td>76.2</td><td>12,648 (2.1)</td></tr><tr><td>Lymphadenopathy</td><td>72.8</td><td>311 (0.2)</td><td>71.8</td><td>843 (0.1)</td></tr><tr><td>Muscle/Joint Pain</td><td>73.5</td><td>5706 (3.5)</td><td>70.1</td><td>33,181 (5.6)</td></tr><tr><td>Runny Nose</td><td>74.1</td><td>865 (0.5)</td><td>71.5</td><td>6,058 (1.0)</td></tr><tr><td>Seizures</td><td>71.2</td><td>573 (0.4)</td><td>70.1</td><td>2,109 (0.4)</td></tr><tr><td>Severe Dehydration</td><td>85.5</td><td>4,400 (2.7)</td><td>84.2</td><td>9,369 (1.6)</td></tr><tr><td>Shortness of Breath</td><td>67.6</td><td>37,074 (22.7)</td><td>65.9</td><td>116,392 (19.7)</td></tr><tr><td>Skin Rash</td><td>72.1</td><td>1593 (1.0)</td><td>70.1</td><td>4028 (0.7)</td></tr><tr><td>Sore Throat</td><td>74.0</td><td>2115 (1.3)</td><td>71.4</td><td>12,826 (2.2)</td></tr><tr><td>Vomiting</td><td>70.6</td><td>6506 (4.0)</td><td>68.4</td><td>35,578 (6.0)</td></tr><tr><td>Wheezing</td><td>72.3</td><td>4077 (2.5)</td><td>70.2</td><td>9779 (1.7)</td></tr><tr><td>Lab Measurements</td><td></td><td></td><td></td><td></td></tr><tr><td>D-dimer (µg/mL)</td><td>99.1</td><td>1.1 (0.5, 2.5)</td><td>98.6</td><td>0.7 (0.4, 1.3)</td></tr><tr><td>ALT (IU/L)</td><td>85.3</td><td>36.0 (22.0, 60.0)</td><td>85.2</td><td>27.0 (17.0, 45.0)</td></tr><tr><td>Bilirubin (µmol/L)</td><td>84.3</td><td>11.0 (8.0, 15.6)</td><td>85.2</td><td>9.0 (7.0, 14.0)</td></tr><tr><td>CRP (mg/L)</td><td>78.9</td><td>115.0 (59.0, 191.8)</td><td>78.3</td><td>74.9 (29.0, 143.0)</td></tr><tr><td>Lymphocytes (10<sup>3</sup>µL)</td><td>78.3</td><td>0.9 (0.6, 1.3)</td><td>76.7</td><td>0.9 (0.6, 1.3)</td></tr><tr><td>Neutrophils (10<sup>9</sup>L)</td><td>78.3</td><td>7.1 (4.8, 10.1)</td><td>76.7</td><td>5.5 (3.8, 8.2)</td></tr><tr><td>Platelets (10<sup>9</sup>L)</td><td>86.7</td><td>13.0 (11.5, 14.7)</td><td>87.3</td><td>12.8 (11.2, 14.3)</td></tr><tr><td>Blood Urea Nitrogen (mmol/L)</td><td>78.9</td><td>6.5 (4.7, 9.7)</td><td>78.5</td><td>6.4 (4.5, 10.0)</td></tr><tr><td>White Blood Cells (10<sup>9</sup>L)</td><td>76.6</td><td>8.8 (6.2, 12.1)</td><td>75.3</td><td>7.2 (5.3, 10.1)</td></tr><tr><td>Vital Signs</td><td></td><td></td><td></td><td></td></tr><tr><td>Diastolic BP (mmHg)</td><td>63.1</td><td>72.9 (16.3)</td><td>64.3</td><td>75.5 (14.8)</td></tr><tr><td>Systolic BP (mmHg)</td><td>63.0</td><td>130.0 (26.3)</td><td>64.2</td><td>131.0 (24.0)</td></tr><tr><td>Heart Rate (bpm)</td><td>63.2</td><td>92.5 (22.3)</td><td>65.3</td><td>91.8 (21.2)</td></tr><tr><td>Oxygen Saturation (%)</td><td>63.3</td><td>91.3 (10.2)</td><td>64.3</td><td>93.8 (9.2)</td></tr><tr><td>Respiratory Rate (brpm)</td><td>63.4</td><td>24.4 (7.7)</td><td>64.7</td><td>22.3 (6.6)</td></tr><tr><td>Temperature (°C)</td><td>63.1</td><td>37.2 (1.1)</td><td>63.9</td><td>37.2 (1.0)</td></tr><tr><td>PE</td><td></td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>99.2</td><td>1365 (0.8)</td><td>99.3</td><td>4291 (0.7)</td></tr></tbody></table>

patients with PE, chest pain, asthma, or fever seemed to have lower risk associated which could be due to earlier and easier detection of these symptoms and conditions in the progression of the disease.

Seeking to combine this clinically insightful information for outcome prediction, we developed a fast prediction model with XGBoost for both PE and death in COVID-19 hospitalised patients, and tested it in different countries separately. We also showed that appropriate class weighting can help with class imbalance and even outperform ensemble resampling methods without having to sacrifice the interpretability of the model (Tables 10, 13). The differences between measured performance on UK and Spain test sets as evaluated by sensitivity and accuracy are due to different class-imbalance ratios and positive case distributions between the datasets. It is important to note that the metric to focus on for our purposes are the validation and test AUC which
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Adjusted odds ratios for PE with 95% confidence intervals.</figcaption></figure>

remain consistent between the two datasets at around 75% as it is the most robust metric in the face of extreme class-imbalance. Since the class-imbalance varies between the two datasets as well, other metrics like sensitivity and accuracy will be significantly impacted despite attempts to dampen it but due to only a few percentages of positive cases, the potency of our approaches can only be limited. The best-performing model for PE prediction evaluated across separate held-out UK-only data, Spain-only data, and UK and Spain data combined is XGBoost without undersampling and without rigid thresholding using robust class weighting. As for death, the XGBoost again outperformed all other models including the ensemble with XGBoost on some metrics.

Since our XGBoost model outperformed other methods, we also showed that the best method for handling class imbalance is through robust class weighting and compared it to other methods for imbalance handling like ensembles and resampling methods. Another advantage of this method is that it avoids introducing bias like in the case of resampling. Finally, XGBoost provides feature importances which was useful for explaining clinical risk prediction of the model to healthcare professionals and policy-makers.

Exploring two different interpretability methods for XGBoost, average gain across splits and Shapley values, showed that the time of dominant presence of the alpha variant, age, fever, shortness of breath, and hypertension were the key predictors for PE, followed by region of admission, sex, and chest pain. Recent work has alluded to an association between the alpha variant and occurrence of thromboembolisms in mice but further research relevant to human samples is missing¹⁸. Age was a complex non-linear predictor with different age groups corresponding to varying risks. The clear colour separation for the Shapley values for age in Fig. 8 showed how each age group has a clearly separable predictive value for mortality with older groups having higher risk but which is not the case for PE as younger age groups can be more predictive of higher PE risk. Furthermore, Shapley values analysis identified obesity, smoking, and the presence of cough as important predictors for PE whereas the default XGBoost method does not. The most predictive features for all-cause mortality were age, region of hospital admission, sex, diabetes, and shortness of breath whereas the default method highlights hypertension and obesity in addition. For mortality, higher values of region corresponded to samples from South Asia and
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Adjusted odds ratios for death with 95% confidence intervals.</figcaption></figure>

South Africa. Comparing all of the top identified predictors across these models for all outcomes can be seen in Tables 16 and 17 where certain symptoms and comorbidities have been identified to be universally predictive risk factors right at-admission without any additional measurements having to be taken for PE and mortality risk assessment.

The pulmonary embolism and mortality prediction model can help with management of COVID-19 as it uses standard demographics, comorbidity, and symptom data collected at admission for identifying patients most at risk of developing PE which may enable an earlier start of targeted anticoagulation therapy. Our mortality risk prediction model can also help with patient population risk assessment and prioritisation across different regions of the world.

A strength of the current study is that a combination of machine learning and traditional statistical modeling can offer a more reliable system for predictive risk forecasting. XGBoost provides at-admission prediction of both events, while odds and hazards ratios obtained from logistic regression and the Cox proportional hazards model give us an insight into stratified risk and global feature importance. We systematically compare our XGBoost model with different risk prediction algorithms. Our model also outperforms recently published results across a variety of metrics like AUROC and sensitivity despite being developed on a much larger and more heterogeneous and diverse dataset while being robust to class imbalance¹⁹. With existing scores built on non-COVID-19 data like The National Early Warning Score 2, there is insufficient information available on their reliability in the COVID-19 setting, and some have been found to underestimate mortality²⁰. Our model is able to deploy at admission for both PE and death risk prediction and can help supplant these needs rapidly.

The study, however, has several limitations. First, almost 60% of patients who died did so in South Africa, and over 70% of PE cases were located in the UK. This may be due to limited access to D-dimer tests or CT scans. There were no mandatory diagnostic criteria in the ISARIC CRF for PE. The absence of a control group of patients without COVID-19 in this dataset prevented estimation of specificity. The patient cohort comprised of hospitalised patients with confirmed COVID-19 who had a mortality rate of 21.7%. These models are not for
<table><caption>**Table 5.** Adjusted odds ratios of features with 95% confidence intervals (only Spain and UK patients included for PE).</caption><thead><tr><th rowspan="2">Feature</th><th colspan="2">PE</th><th colspan="2">Death</th></tr><tr><th>OR (95% CI)</th><th>P value</th><th>OR (95% CI)</th><th>P value</th></tr></thead><tbody><tr><td>Age</td><td></td><td>&lt; 0.005</td><td>1.0</td><td>&lt; 0.005</td></tr><tr><td>&lt; 20</td><td>1.0</td><td>&lt; 0.005</td><td>1.0</td><td>&lt; 0.005</td></tr><tr><td>20-40</td><td>5.5 (3.3, 10.1)</td><td>&lt; 0.005</td><td>2.4 (2.3, 2.6)</td><td>&lt; 0.005</td></tr><tr><td>40-60</td><td>8.0 (4.8, 14.6)</td><td>&lt; 0.005</td><td>6.8 (6.4, 7.3)</td><td>&lt; 0.005</td></tr><tr><td>60-80</td><td>8.2 (5.0, 15.1)</td><td>&lt; 0.005</td><td>16.1 (15.1, 17.2)</td><td>&lt; 0.005</td></tr><tr><td>&gt; 80</td><td>5.7 (3.4, 10.5)</td><td>&lt; 0.005</td><td>27.8 (26.0, 29.7)</td><td>&lt; 0.005</td></tr><tr><td>Sex (male)</td><td>1.3 (1.3, 1.4)</td><td>&lt; 0.005</td><td>1.2 (1.2, 1.3)</td><td>&lt; 0.005</td></tr><tr><td>Region</td><td></td><td></td><td></td><td>&lt; 0.005</td></tr><tr><td>Sub-saharan Africa</td><td>-</td><td>-</td><td>3.3 (3.0, 3.6)</td><td>&lt; 0.005</td></tr><tr><td>Europe and Central Asia</td><td>-</td><td>-</td><td>0.9 (0.8, 1.0)</td><td>0.052</td></tr><tr><td>South Asia</td><td>-</td><td>-</td><td>4.0 (3.7, 4.4)</td><td>&lt; 0.005</td></tr><tr><td>East Asia</td><td>-</td><td>-</td><td>1.0</td><td>-</td></tr><tr><td>North America</td><td>-</td><td>-</td><td>1.2 (1.1, 1.3)</td><td>&lt; 0.005</td></tr><tr><td>MENA</td><td>-</td><td>-</td><td>2.8 (2.4, 3.3)</td><td>&lt; 0.005</td></tr><tr><td>Alpha variant</td><td>2.6 (2.4, 2.8)</td><td>&lt; 0.005</td><td></td><td></td></tr><tr><td>Comorbidities</td><td></td><td></td><td></td><td></td></tr><tr><td>AIDS/HIV</td><td>0.9 (0.5, 1.5)</td><td>0.553</td><td>1.5 (1.4, 1.5)</td><td>&lt; 0.005</td></tr><tr><td>Asthma</td><td>0.9 (0.9, 1.0)</td><td>0.055</td><td>0.8 (0.8, 0.9)</td><td>&lt; 0.005</td></tr><tr><td>Chronic cardiac disease</td><td>0.7 (0.6, 0.8)</td><td>&lt; 0.005</td><td>1.2 (1.2, 1.2)</td><td>&lt; 0.005</td></tr><tr><td>Chronic haematological</td><td>0.9 (0.8, 1.1)</td><td>0.320</td><td>1.1 (1.0, 1.1)</td><td>0.016</td></tr><tr><td>Chronic kidney disease</td><td>0.9 (0.8, 1.0)</td><td>&lt; 0.005</td><td>1.4 (1.3, 1.4)</td><td>&lt; 0.005</td></tr><tr><td>Chronic neurological</td><td>0.9 (0.8, 1.0)</td><td>0.087</td><td>1.1 (1.1, 1.1)</td><td>&lt; 0.005</td></tr><tr><td>Chronic pulmonary</td><td>0.9 (0.8, 0.9)</td><td>&lt; 0.005</td><td>1.2 (1.2, 1.2)</td><td>&lt; 0.005</td></tr><tr><td>Dementia</td><td>0.7 (0.6, 0.8)</td><td>&lt; 0.005</td><td>1.3 (1.2, 1.3)</td><td>&lt; 0.005</td></tr></tbody></table>

use in the community and could still perform differently in populations at lower risk of death and across different regions of the world. As part of future work, dependent on sufficient data, PE and death could be modelled with a comprehensive multi-state statistical framework, which incorporates the interrelations among survival, PE, and death states.

In conclusion, the set of decisions taken must include different stakeholders like patients, clinicians, hospital administrators, researchers, and data procurers so that trade-offs can be identified and context-informed decisions can be taken to address them, especially if our models could have missed harms or benefits to different groups and communities.

## Methods

### Data

In this work, we use data of COVID-19 patients from The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC), a repository that standardises and secures data on COVID-19 assembled from a global cohort over 2 years of the pandemic as of January 2022. It includes so far data on over 800,000 patients from 53 countries. These data capture the global experience of the first 2 years of the pandemic²¹. The clinical characterisation protocol underwent ethical review by the World Health Organization Ethics Review Committee and ethics approval was obtained for each participating country and site according to local requirements. Ethics Committee approval was given by the WHO Ethics Review Committee (RPC571 and RPC572, 25 April 2013). Institutional approval was additionally obtained by participating sites including the South Central-Oxford C Research Ethics Committee in England (Ref. 13/SC/0149), the Scotland A Research Ethics Committee (Ref. 20/SS/0028) for the UK and the Human Research Ethics Committee (Medical) at the University of the Witwatersrand in South Africa as part of a national surveillance programme (M160667), which collectively represent the majority of the data. Other institutional and national approvals are in place as per local requirements. This is a secondary analysis of data collected, with appropriate local permissions, and each institution signed a Terms of Submission in which committed that they had the appropriate permissions in place. All methods were performed in accordance with the relevant guidelines and regulations.

The study population consisted of all patients with either clinically diagnosed or laboratory confirmed COVID-19 admitted to the participating hospitals. The aim of the recruiting sites was to use a consecutive sample.

The dataset contains 800,459 patients and 182 variables. The mean age of patients was 56.4 (20.9), 48.6% were male, and the majority of cases were from South Africa (54.0%) and the United Kingdom (34.1%). 65.3% of patients were discharged alive and 20.4% died. We grouped countries with fewer than 60 individuals into a single category. Out of all patients, 5450 (0.7%) experienced a pulmonary embolism, 73 experienced thromboembolism,
<table><caption>**Table 6.** Adjusted odds ratios of features with 95% confidence intervals (only Spain and UK patients included for PE).</caption><thead><tr><th rowspan="2">Feature</th><th colspan="2">PE</th><th colspan="2">Death</th></tr><tr><th>OR (95% CI)</th><th>P Value</th><th>OR (95% CI)</th><th>P Value</th></tr></thead><tbody><tr><td>Diabetes</td><td>0.8 (0.8, 0.9)</td><td>&lt; 0.005</td><td>1.3 (1.3, 1.3)</td><td>&lt; 0.005</td></tr><tr><td>Hypertension</td><td>1.3 (1.2, 1.4)</td><td>&lt; 0.005</td><td>1.0 (1.0, 1.0)</td><td>&lt; 0.005</td></tr><tr><td>Liver disease</td><td>0.9 (0.7, 1.1)</td><td>0.200</td><td>1.3 (1.2, 1.4)</td><td>&lt; 0.005</td></tr><tr><td>Malignant neoplasm</td><td>1.3 (1.2, 1.4)</td><td>&lt; 0.005</td><td>1.4 (1.4, 1.5)</td><td>&lt; 0.005</td></tr><tr><td>Malnutrition</td><td>0.8 (0.6, 1.1)</td><td>0.200</td><td>1.2 (1.1, 1.2)</td><td>&lt; 0.005</td></tr><tr><td>Obesity</td><td>1.4 (1.3, 1.6)</td><td>&lt; 0.005</td><td>1.2 (1.1, 1.2)</td><td>&lt; 0.005</td></tr><tr><td>Rheumatologic</td><td>1.1 (0.9, 1.2)</td><td>0.137</td><td>0.9 (0.9, 1.0)</td><td>&lt; 0.005</td></tr><tr><td>Smoking</td><td>1.1 (1.0, 1.2)</td><td>&lt; 0.005</td><td>1.0 (1.0, 1.0)</td><td>0.126</td></tr><tr><td>Symptoms</td><td></td><td></td><td></td><td></td></tr><tr><td>Symptomatic</td><td>1.6 (1.4, 1.9)</td><td>&lt;0.005</td><td>1.1 (1.1, 1.1)</td><td>&lt; 0.005</td></tr><tr><td>Abdominal pain</td><td>0.8 (0.7, 0.9)</td><td>&lt;0.005</td><td>0.9 (0.9, 1.0)</td><td>&lt; 0.005</td></tr><tr><td>Confusion</td><td>0.9 (0.8, 1.0)</td><td>0.009</td><td>1.5 (1.4, 1.5)</td><td>&lt; 0.005</td></tr><tr><td>Bleeding</td><td>1.3 (1.0, 1.6)</td><td>0.011</td><td>1.1 (1.0, 1.2)</td><td>0.025</td></tr><tr><td>Chest pain</td><td>1.8 (1.6, 1.9)</td><td>&lt;0.005</td><td>0.7 (0.7, 0.8)</td><td>&lt; 0.005</td></tr><tr><td>Conjunctivitis</td><td>1.6 (0.8, 2.9)</td><td>0.260</td><td>0.8 (0.6, 1.0)</td><td>0.050</td></tr><tr><td>Cough</td><td>0.9 (0.9, 1.0)</td><td>0.391</td><td>0.9 (0.9, 1.0)</td><td>&lt; 0.005</td></tr><tr><td>Diarrhoea</td><td>0.9 (0.9, 1.1)</td><td>0.568</td><td>0.9 (0.9, 0.9)</td><td>&lt; 0.005</td></tr><tr><td>Ear pain</td><td>0.7 (0.3, 1.5)</td><td>0.245</td><td>1.1 (0.9, 1.4)</td><td>0.495</td></tr><tr><td>Fatigue</td><td>1.1 (1.1, 1.2)</td><td>&lt; 0.005</td><td>1.0 (0.9, 1.0)</td><td>&lt; 0.005</td></tr><tr><td>Headache</td><td>0.7 (0.7, 0.8)</td><td>&lt; 0.005</td><td>0.7 (0.6, 0.7)</td><td>&lt; 0.005</td></tr><tr><td>Fever</td><td>0.8 (0.7, 0.9)</td><td>&lt; 0.005</td><td>1.1 (1.0, 1.1)</td><td>&lt; 0.005</td></tr><tr><td>Lost sense of smell</td><td>1.2 (1.0, 1.4)</td><td>0.012</td><td>0.7 (0.6, 0.7)</td><td>&lt; 0.005</td></tr><tr><td>Lost sense of taste</td><td>1.0 (0.9, 1.2)</td><td>0.490</td><td>0.9 (0.8, 0.9)</td><td>&lt; 0.005</td></tr><tr><td>Lymphadenopathy</td><td>1.7 (1.1, 2.6)</td><td>&lt; 0.005</td><td>1.4 (1.2, 1.7)</td><td>&lt; 0.005</td></tr><tr><td>Muscle/joint pain</td><td>0.9 (0.6, 1.0)</td><td>&lt; 0.005</td><td>0.8 (0.8, 0.8)</td><td>&lt; 0.005</td></tr><tr><td>Runny nose</td><td>0.9 (0.6, 1.3)</td><td>0.832</td><td>0.9 (0.8, 1.0)</td><td>0.042</td></tr><tr><td>Seizures</td><td>0.5 (0.3, 0.8)</td><td>&lt; 0.005</td><td>1.0 (0.9, 1.1)</td><td>0.784</td></tr><tr><td>Severe dehydration</td><td>1.2 (1.0, 1.4)</td><td>&lt; 0.005</td><td>1.3 (1.2, 1.3)</td><td>&lt; 0.005</td></tr></tbody></table>

<table><caption>**Table 7.** Adjusted odds ratios of features with 95% confidence intervals (only Spain and UK patients included for PE).</caption><thead><tr><th rowspan="2">Feature</th><th colspan="2">PE</th><th colspan="2">Death</th></tr><tr><th>OR (95% CI)</th><th>P Value</th><th>OR (95% CI)</th><th>P Value</th></tr></thead><tbody><tr><td>Shortness of breath</td><td>2.4 (2.2, 2.6)</td><td>&lt; 0.005</td><td>1.8 (1.7, 1.8)</td><td>&lt; 0.005</td></tr><tr><td>Skin rash</td><td>1.1 (0.9, 1.4)</td><td>0.842</td><td>1.3 (1.2, 1.4)</td><td>&lt; 0.005</td></tr><tr><td>Sore throat</td><td>0.7 (0.6, 0.8)</td><td>&lt; 0.005</td><td>1.0 (0.9, 1.0)</td><td>0.181</td></tr><tr><td>Vomiting</td><td>0.9 (0.8, 1.0)</td><td>&lt; 0.005</td><td>0.9 (0.8, 0.9)</td><td>&lt; 0.005</td></tr><tr><td>Wheezing</td><td>0.8 (0.7, 1.0)</td><td>&lt; 0.005</td><td>1.3 (1.2, 1.3)</td><td>&lt; 0.005</td></tr><tr><td>PE</td><td>-</td><td>-</td><td>1.3 (1.2, 1.4)</td><td>&lt; 0.005</td></tr></tbody></table>

and 143 experienced deep vein thrombosis. We define our outcome of interest as the main pulmonary embolism (PE) diagnosis for subsequent analysis. 4653 (82.1%) of the PE cases were recorded in the United Kingdom (UK) and Spain which based on our knowledge makes our study the largest study of its kind for PE to date. Due to similar data collection patterns and recording, we used data from these two countries only for PE modelling as they contain the vast majority of reported PE cases.

## Data preprocessing

Since treatment information does not have reliable timestamps for most patients, the following variables were used in the analysis for PE: demographics (including age, sex, country), comorbidities (hypertension, diabetes, smoking etc.), and symptoms (coughing, fever, fatigue etc.). The presence of diagnosis during domination by the alpha variant was also included (after December 2020) due to its possible association with incidence of PE.
<figure><img src="image_5.png" /></figure>

# Events: 160302: Global p-value (Log-Rank): 0

AIC: 4186239.48; Concordance Index: 0.71

<caption>**Figure 3.** Adjusted hazard ratios for mortality with 95% confidence intervals.</caption>

In our modelling of PE, we used data from patients from the UK and Spain only and did not use laboratory measurements or imputation methods. 269,373 patients and 45 variables remained for PE, and 734,282 patients and 55 variables for death. Age was grouped into 5 categories (0–20, 20–40, 40–60, 60–80, 80–120) with the distributions seen in Figs. 13 and 14 below. The *symptomatic* variable represents any symptoms reported for a patient. For number of days from admission to event (death), we removed outliers of more than 200 days and those in the negatives, thereby removing 1342 patients.

Prior to processing the data, for PE prediction, we held out 3 test sets of 20% of the total dataset for independent testing, one of which would only include patients from Spain, one only including patients from the UK, and another including both. For mortality prediction testing, we held out 20% of the total dataset sample. A workflow diagram for data processing and system design is shown in Fig. 15.

Stratified Kaplan–Meier curves by age, sex, and region of admission were plotted using Cox proportional hazards models while machine learning methods were applied for prediction of PE or death.

## Baseline and machine learning methods

The reference groups for statistical analysis for age were those under 20 years old, for region it was East Asia, and for country variable it was Norway. For Cox proportional hazards model, proportionality assumption was verified through visualisation of the survival curves and observing parallel behaviour as seen in Fig. 4. We investigated several prediction methods for PE occurrence and death, including logistic regression, Linear Discriminant Analysis (LDA), naive Bayes classifier, random forests, ADABoosting algorithms, and the high-performing extreme gradient boost machine (XGBoost)$^{22,23}$. Previous studies looking at tree-based algorithms
<table><caption>**Table 8.** Adjusted hazard ratios for mortality.</caption><thead><tr><th>Feature</th><th>HR</th><th>95% CI</th><th>P value</th></tr></thead><tbody><tr><td>Age</td><td></td><td></td><td></td></tr><tr><td>20-40</td><td>2.5</td><td>2.3, 2.7</td><td>&lt; 0.005</td></tr><tr><td>40-60</td><td>6.5</td><td>6.1, 7.0</td><td>&lt; 0.005</td></tr><tr><td>60-80</td><td>14.2</td><td>13.3, 15.1</td><td>&lt; 0.005</td></tr><tr><td>&gt; 80</td><td>21.3</td><td>20.0, 22.8</td><td>&lt; 0.005</td></tr><tr><td>Sex (male)</td><td>1.2</td><td>1.2, 1.2</td><td>&lt; 0.005</td></tr><tr><td>Region</td><td></td><td></td><td></td></tr><tr><td>Sub-saharan Africa</td><td>2.6</td><td>2.4, 2.8</td><td>&lt; 0.005</td></tr><tr><td>Europe and Central Asia</td><td>0.9</td><td>0.8, 0.9</td><td>&lt; 0.005</td></tr><tr><td>South Asia</td><td>2.9</td><td>2.6, 3.1</td><td>&lt; 0.005</td></tr><tr><td>North America</td><td>1.0</td><td>0.9, 1.1</td><td>0.557</td></tr><tr><td>MENA</td><td>1.8</td><td>1.6, 2.0</td><td>&lt; 0.005</td></tr><tr><td>Comorbidities</td><td></td><td></td><td></td></tr><tr><td>AIDS/HIV</td><td>1.4</td><td>1.4, 1.5</td><td>&lt; 0.005</td></tr><tr><td>Asthma</td><td>0.9</td><td>0.8, 0.9</td><td>&lt; 0.005</td></tr><tr><td>Chronic cardiac disease</td><td>1.2</td><td>1.1, 1.2</td><td>&lt; 0.005</td></tr><tr><td>Chronic haematological</td><td>1.1</td><td>1.0, 1.1</td><td>0.006</td></tr><tr><td>Chronic kidney disease</td><td>1.3</td><td>1.3, 1.3</td><td>&lt; 0.005</td></tr><tr><td>Chronic neurological</td><td>1.1</td><td>1.0, 1.1</td><td>&lt; 0.005</td></tr><tr><td>Chronic pulmonary</td><td>1.1</td><td>1.1, 1.2</td><td>&lt; 0.005</td></tr><tr><td>Dementia</td><td>1.2</td><td>1.2, 1.2</td><td>&lt; 0.005</td></tr><tr><td>Diabetes</td><td>1.2</td><td>1.2, 1.2</td><td>&lt; 0.005</td></tr><tr><td>Hypertension</td><td>1.0</td><td>1.0, 1.0</td><td>0.853</td></tr><tr><td>Immunosuppression</td><td>1.2</td><td>1.2, 1.3</td><td>&lt; 0.005</td></tr><tr><td>Liver disease</td><td>1.2</td><td>1.2, 1.3</td><td>&lt; 0.005</td></tr><tr><td>Malignant neoplasm</td><td>1.3</td><td>1.2, 1.3</td><td>&lt; 0.005</td></tr><tr><td>Malnutrition</td><td>1.1</td><td>1.0, 1.2</td><td>&lt; 0.005</td></tr><tr><td>Obesity</td><td>1.2</td><td>1.1, 1.2</td><td>&lt; 0.005</td></tr><tr><td>Rheumatologic</td><td>0.9</td><td>0.9, 1.0</td><td>&lt; 0.005</td></tr><tr><td>Smoking</td><td>1.0</td><td>1.0, 1.0</td><td>0.082</td></tr><tr><td>Tuberculosis</td><td>1.1</td><td>1.1, 1.2</td><td>&lt; 0.005</td></tr></tbody></table>

such as XGBoost have highlighted its capacity to learn the correlations between covariates well when it comes to mortality prediction in COVID-19 patients while also being somewhat interpretable²⁴. We applied all of these methods for the purposes of a systematic comparison using 5-fold cross-validation, several hold-out test sets stratified across countries and regions, and evaluated with multiple metrics. A list of methods applied can be seen in Table 18 with details in Supplementary.

As is often the case in disease prediction, there is class imbalance with about 1.7% of UK and Spain patients having been diagnosed with PE and around 20.4% having died in the case population. To address this, we use other metrics mentioned above in the evaluation of our models besides accuracy as it does not capture the true predictive performance of our models and we rely more on sensitivity and the F1 score. We also use a different threshold for prediction after probability estimation instead of the default 0.5 to achieve cost-sensitivity, and we apply random undersampling at a minority:majority ratio of 1:4 as has been highlighted in other work²⁵,²⁶. We evaluate these methods both separately and in combination to investigate the best approach for this set of prediction problems.

To address imbalance in predictions, we applied either undersampling, thresholding, or both. As for death, due to a much softer imbalance, undersampling was not necessary. We also added class-weighting to our XGBoost model using inverse proportions and compared it with the other methods to address class imbalance. All confusion matrices and parameter details for each model can be found in the Supplementary.

Furthermore, we build an ensemble that combines AdaBoosted decision trees with robust undersampling using different subsets for resampled training so as to address the imbalance and compare this cost-sensitive model with our best performing model and add further confidence in its ability to generalise in an imbalanced scenario²⁷. We extend the ensemble learning methods by using our own XGBoost model in the ensemble structure instead of the standard AdaBoosted decision trees. The number of trees was a tunable hyperparameter listed in the Supplementary (Tables IV–VI). We compare our cost-sensitive class-weighted XGBoost machine learning model with these resampling ensembles to show improved performance without the need of introducing bias like in the case of resampling while maintaining interpretability.
<table><caption>**Table 9.** Adjusted hazard ratios for mortality (continued).</caption><thead><tr><th>Feature</th><th>HR</th><th>95% CI</th><th>P value</th></tr></thead><tbody><tr><td>Symptoms</td><td></td><td></td><td></td></tr><tr><td>Symptomatic</td><td>1.1</td><td>1.1, 1.2</td><td>&lt;0.005</td></tr><tr><td>Abdominal pain</td><td>0.9</td><td>0.9, 1.0</td><td>&lt;0.005</td></tr><tr><td>Confusion</td><td>1.3</td><td>1.3, 1.3</td><td>&lt;0.005</td></tr><tr><td>Bleeding</td><td>1.1</td><td>1.0, 1.1</td><td>0.146</td></tr><tr><td>Chest pain</td><td>0.8</td><td>0.7, 0.8</td><td>&lt;0.005</td></tr><tr><td>Conjunctivitis</td><td>0.8</td><td>0.7, 1.0</td><td>0.028</td></tr><tr><td>Cough</td><td>1.0</td><td>0.9, 1.0</td><td>&lt;0.005</td></tr><tr><td>Diarrhoea</td><td>0.9</td><td>0.9, 0.9</td><td>&lt;0.005</td></tr><tr><td>Ear pain</td><td>1.1</td><td>0.9, 1.3</td><td>0.655</td></tr><tr><td>Fatigue</td><td>1.0</td><td>0.9, 1.0</td><td>&lt;0.005</td></tr><tr><td>Headache</td><td>0.7</td><td>0.7, 0.7</td><td>&lt;0.005</td></tr><tr><td>Fever</td><td>1.1</td><td>1.1, 1.1</td><td>&lt;0.005</td></tr><tr><td>Lost sense of smell</td><td>0.7</td><td>0.7, 0.8</td><td>&lt;0.005</td></tr><tr><td>Lost sense of taste</td><td>0.9</td><td>0.8, 0.9</td><td>&lt;0.005</td></tr><tr><td>Lymphadenopathy</td><td>1.2</td><td>1.1, 1.4</td><td>&lt;0.005</td></tr><tr><td>Muscle/joint pain</td><td>0.9</td><td>0.8, 0.9</td><td>&lt;0.005</td></tr><tr><td>Runny nose</td><td>1.0</td><td>0.9, 1.0</td><td>0.121</td></tr><tr><td>Seizures</td><td>1.0</td><td>0.9, 1.1</td><td>0.764</td></tr><tr><td>Severe dehydration</td><td>1.2</td><td>1.2, 1.2</td><td>&lt;0.005</td></tr><tr><td>Shortness of breath</td><td>1.6</td><td>1.6, 1.7</td><td>&lt;0.005</td></tr><tr><td>Skin rash</td><td>1.2</td><td>1.1, 1.2</td><td>&lt;0.005</td></tr><tr><td>Sore throat</td><td>1.0</td><td>0.9, 1.0</td><td>0.223</td></tr><tr><td>Vomiting</td><td>0.9</td><td>0.8, 0.9</td><td>&lt;0.005</td></tr><tr><td>Wheezing</td><td>1.2</td><td>1.2, 1.2</td><td>&lt;0.005</td></tr><tr><td>PE</td><td>1.2</td><td>1.2, 1.3</td><td>&lt;0.005</td></tr></tbody></table>

## Model validation and evaluation

We proceed to tune our machine learning models and validate them using stratified 5-fold cross-validation with Bayesian optimisation. We repeated the optimisation procedure for 50 iterations after which we evaluated the model on the independent test set with the following metrics: AUROC, Accuracy, Weighted F1 Score, and Sensitivity. The details can be found in the Supplementary.

While existing studies referenced in the Introduction mention some approaches to feature importance estimation for COVID-19 mortality and outcome prediction as well as for other problems, rarely does one find several interpretability methods compared and contrasted in one scenario. We implemented both tree-based F-score interpretability methods as well as Shapley values analysis, logistic regression and Cox regression, and hope to draw interesting conclusions from each and their comparisons²⁸,²⁹. A full explanation for the Shapley values method and its details can be found in the Supplementary materials.

## Role of the funding source

The funder had no role in study design, data collection, data analysis, data interpretation, writing of the report, and decision to submit the paper for publication.
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Kaplan–Meier survival curve for COVID-19 patients stratified by a) age, b) sex, and c) region.</figcaption></figure>
<table><caption>**Table 10.** Prediction model results for PE on test set with UK and Spain (F1-w is the weighted F1 score). Best performing model performance values are in bold.</caption><thead><tr><th>Models</th><th>Validation AUC</th><th>AUC</th><th>Accuracy</th><th>F1-w</th><th>Sensitivity</th></tr></thead><tbody><tr><td colspan="6">No undersampling</td></tr><tr><td colspan="6">No threshold</td></tr><tr><td>Logistic regression</td><td>72.5</td><td>71.0</td><td>64.0</td><td>76.5</td><td>69.3</td></tr><tr><td>LDA</td><td>72.2</td><td>70.6</td><td>98.3</td><td>97.4</td><td>0.0</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>69.1</td><td>98.1</td><td>97.4</td><td>0.9</td></tr><tr><td>Random forest</td><td>73.6</td><td>73.5</td><td>65.4</td><td>77.5</td><td>69.7</td></tr><tr><td>Stacking Ensemble</td><td>63.0</td><td>67.3</td><td>65.5</td><td>77.6</td><td>69.1</td></tr><tr><td>Ensemble</td><td>73.0</td><td>71.8</td><td>63.7</td><td>76.2</td><td>70.8</td></tr><tr><td>Ensemble (XGBoost)</td><td>73.6</td><td>73.8</td><td>64.5</td><td>76.9</td><td>70.0</td></tr><tr><td><strong>XGBoost</strong></td><td><strong>75.6</strong></td><td><strong>75.9</strong></td><td><strong>72.3</strong></td><td><strong>82.3</strong></td><td><strong>67.5</strong></td></tr><tr><td colspan="6">No undersampling</td></tr><tr><td colspan="6">With threshold</td></tr><tr><td>Logistic Regression</td><td>72.5</td><td>66.7</td><td>63.0</td><td>75.7</td><td>70.5</td></tr><tr><td>LDA</td><td>72.2</td><td>66.5</td><td>66.7</td><td>78.5</td><td>66.3</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>65.3</td><td>61.8</td><td>74.9</td><td>68.9</td></tr><tr><td>Random forest</td><td>73.6</td><td>66.6</td><td>71.4</td><td>81.8</td><td>61.5</td></tr><tr><td>XGBoost</td><td>73.8</td><td>67.3</td><td>67.9</td><td>79.3</td><td>66.7</td></tr><tr><td colspan="6">With undersampling</td></tr><tr><td colspan="6">No threshold</td></tr><tr><td>Logistic Regression</td><td>72.4</td><td>71.0</td><td>63.9</td><td>76.5</td><td>69.1</td></tr><tr><td>LDA</td><td>72.2</td><td>70.6</td><td>95.9</td><td>96.4</td><td>9.8</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>69.0</td><td>82.7</td><td>89.0</td><td>34.2</td></tr><tr><td>Random forest</td><td>74.3</td><td>73.9</td><td>68.3</td><td>79.6</td><td>66.6</td></tr><tr><td>Stacking Ensemble</td><td>64.5</td><td>67.6</td><td>66.8</td><td>78.6</td><td>68.4</td></tr><tr><td>XGBoost</td><td>73.8</td><td>73.7</td><td>66.1</td><td>78.1</td><td>69.0</td></tr><tr><td colspan="6">With undersampling</td></tr><tr><td colspan="6">With threshold</td></tr><tr><td>Logistic Regression</td><td>72.4</td><td>66.3</td><td>64.7</td><td>77.1</td><td>67.9</td></tr><tr><td>LDA</td><td>72.2</td><td>66.7</td><td>63.2</td><td>75.9</td><td>70.3</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>65.2</td><td>61.5</td><td>74.6</td><td>68.9</td></tr><tr><td>Random forest</td><td>74.3</td><td>73.9</td><td>68.3</td><td>79.6</td><td>66.6</td></tr><tr><td>XGBoost</td><td>73.8</td><td>67.3</td><td>62.4</td><td>75.3</td><td>72.5</td></tr></tbody></table>
<table><caption>**Table 11.** Prediction model results for PE on UK test set (F1-w is the weighted F1 score). Best performing model performance values are in bold.</caption><thead><tr><th>Models</th><th>Validation AUC</th><th>AUC</th><th>Accuracy</th><th>F1-w</th><th>Sensitivity</th></tr></thead><tbody><tr><td colspan="6">No undersampling</td></tr><tr><td colspan="6">No threshold</td></tr><tr><td>Logistic Regression</td><td>72.5</td><td>69.4</td><td>64.9</td><td>77.3</td><td>65.5</td></tr><tr><td>LDA</td><td>72.2</td><td>69.2</td><td>98.4</td><td>97.6</td><td>0.0</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>67.2</td><td>98.3</td><td>97.6</td><td>0.5</td></tr><tr><td>Random forest</td><td>73.6</td><td>71.2</td><td>65.6</td><td>77.8</td><td>66.0</td></tr><tr><td>Stacking ensemble</td><td>63.0</td><td>65.7</td><td>66.1</td><td>78.2</td><td>65.2</td></tr><tr><td>Ensemble</td><td>73.0</td><td>70.3</td><td>64.7</td><td>77.1</td><td>67.0</td></tr><tr><td>Ensemble (XGBoost)</td><td>73.6</td><td>71.6</td><td>64.9</td><td>77.3</td><td>66.1</td></tr><tr><td><strong>XGBoost</strong></td><td><strong>75.6</strong></td><td><strong>74.5</strong></td><td><strong>73.4</strong></td><td><strong>83.2</strong></td><td><strong>63.5</strong></td></tr><tr><td colspan="6">No undersampling</td></tr><tr><td colspan="6">With threshold</td></tr><tr><td>Logistic regression</td><td>72.5</td><td>65.3</td><td>63.9</td><td>76.5</td><td>66.8</td></tr><tr><td>LDA</td><td>72.2</td><td>65.3</td><td>68.7</td><td>80.0</td><td>61.9</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>63.6</td><td>61.9</td><td>75.0</td><td>65.5</td></tr><tr><td>Random forest</td><td>73.6</td><td>64.5</td><td>71.9</td><td>82.2</td><td>56.9</td></tr><tr><td>XGBoost</td><td>73.8</td><td>65.5</td><td>68.5</td><td>79.9</td><td>62.5</td></tr><tr><td colspan="6">With undersampling</td></tr><tr><td colspan="6">No threshold</td></tr><tr><td>Logistic Regression</td><td>72.4</td><td>69.3</td><td>64.8</td><td>77.2</td><td>65.2</td></tr><tr><td>LDA</td><td>72.2</td><td>69.2</td><td>97.0</td><td>97.0</td><td>4.8</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>67.2</td><td>83.3</td><td>89.5</td><td>29.7</td></tr><tr><td>Random forest</td><td>74.3</td><td>71.7</td><td>68.8</td><td>80.1</td><td>62.4</td></tr><tr><td>Stacking Ensemble</td><td>64.5</td><td>65.9</td><td>67.5</td><td>79.2</td><td>64.3</td></tr><tr><td>XGBoost</td><td>73.8</td><td>71.7</td><td>66.8</td><td>78.6</td><td>65.1</td></tr><tr><td colspan="6">With undersampling</td></tr><tr><td colspan="6">With threshold</td></tr><tr><td>Logistic Regression</td><td>72.4</td><td>64.8</td><td>65.6</td><td>77.8</td><td>64.0</td></tr><tr><td>LDA</td><td>72.2</td><td>65.5</td><td>64.5</td><td>77.0</td><td>66.6</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>63.5</td><td>61.6</td><td>74.8</td><td>65.5</td></tr><tr><td>Random forest</td><td>74.3</td><td>65.5</td><td>69.3</td><td>80.5</td><td>61.6</td></tr><tr><td>XGBoost</td><td>73.8</td><td>65.9</td><td>62.8</td><td>75.7</td><td>69.1</td></tr></tbody></table>
<table><caption>**Table 12.** Prediction model results for PE on Spain test set (F1-w is the weighted F1 score). Best performing model performance values are in bold.</caption><thead><tr><th>Models</th><th>Validation AUC</th><th>AUC</th><th>Accuracy</th><th>F1-w</th><th>Sensitivity</th></tr></thead><tbody><tr><td colspan="6">No undersampling</td></tr><tr><td colspan="6">No threshold</td></tr><tr><td>Logistic Regression</td><td>72.5</td><td>74.5</td><td>48.5</td><td>61.6</td><td>96.5</td></tr><tr><td>LDA</td><td>72.2</td><td>73.6</td><td>96.2</td><td>94.4</td><td>0.0</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>77.6</td><td>95.7</td><td>94.3</td><td>3.5</td></tr><tr><td>Random forest</td><td>73.6</td><td>78.7</td><td>62.1</td><td>73.3</td><td>95.7</td></tr><tr><td>Stacking ensemble</td><td>63.0</td><td>75.7</td><td>56.4</td><td>68.6</td><td>96.5</td></tr><tr><td>Ensemble</td><td>73.0</td><td>74.4</td><td>46.5</td><td>60.0</td><td>95.4</td></tr><tr><td>Ensemble (XGBoost)</td><td>73.6</td><td>79.8</td><td>57.8</td><td>69.8</td><td>95.4</td></tr><tr><td><strong>XGBoost</strong></td><td><strong>75.6</strong></td><td><strong>78.9</strong></td><td><strong>59.6</strong></td><td><strong>67.2</strong></td><td><strong>95.7</strong></td></tr><tr><td colspan="6">No undersampling</td></tr><tr><td colspan="6">With threshold</td></tr><tr><td>Logistic regression</td><td>72.5</td><td>71.3</td><td>48.1</td><td>61.2</td><td>96.5</td></tr><tr><td>LDA</td><td>72.2</td><td>64.2</td><td>33.5</td><td>45.8</td><td>97.4</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>76.3</td><td>60.9</td><td>72.4</td><td>93.0</td></tr><tr><td>Random forest</td><td>73.6</td><td>78.4</td><td>64.1</td><td>74.9</td><td>93.9</td></tr><tr><td>XGBoost</td><td>73.8</td><td>76.0</td><td>57.1</td><td>69.2</td><td>96.5</td></tr><tr><td colspan="6">With undersampling</td></tr><tr><td colspan="6">No threshold</td></tr><tr><td>Logistic regression</td><td>72.4</td><td>74.6</td><td>49.5</td><td>62.6</td><td>96.5</td></tr><tr><td>LDA</td><td>72.2</td><td>73.9</td><td>78.7</td><td>85.0</td><td>44.3</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>77.6</td><td>71.8</td><td>80.5</td><td>66.1</td></tr><tr><td>Random forest</td><td>74.3</td><td>80.2</td><td>58.7</td><td>70.6</td><td>96.5</td></tr><tr><td>Stacking ensemble</td><td>64.5</td><td>75.0</td><td>55.1</td><td>67.5</td><td>96.5</td></tr><tr><td>XGBoost</td><td>73.8</td><td>79.3</td><td>54.9</td><td>67.4</td><td>96.5</td></tr><tr><td colspan="6">With undersampling</td></tr><tr><td colspan="6">With threshold</td></tr><tr><td>Logistic Regression</td><td>72.4</td><td>71.8</td><td>49.7</td><td>62.7</td><td>95.7</td></tr><tr><td>LDA</td><td>72.2</td><td>68.2</td><td>42.0</td><td>55.2</td><td>96.5</td></tr><tr><td>Naive Bayes</td><td>70.4</td><td>76.4</td><td>61.1</td><td>72.5</td><td>93.0</td></tr><tr><td>Random forest</td><td>74.3</td><td>76.9</td><td>58.7</td><td>70.6</td><td>96.5</td></tr><tr><td>XGBoost</td><td>73.8</td><td>74.7</td><td>54.4</td><td>66.9</td><td>96.5</td></tr></tbody></table>
<table><caption>**Table 13.** Death prediction model results for test set (F1-w is the weighted F1 score). Best performing model performance values are in bold.</caption><thead><tr><th>Models</th><th>Validation AUC</th><th>AUC</th><th>Accuracy</th><th>F1-w</th><th>Sensitivity</th></tr></thead><tbody><tr><td colspan="6">No undersampling</td></tr><tr><td colspan="6">No threshold</td></tr><tr><td>Logistic regression</td><td>73.2</td><td>72.9</td><td>66.2</td><td>68.9</td><td>68.4</td></tr><tr><td>LDA</td><td>73.1</td><td>72.9</td><td>78.0</td><td>71.1</td><td>7.8</td></tr><tr><td>Naive Bayes</td><td>71.3</td><td>71.1</td><td>74.9</td><td>72.5</td><td>23.7</td></tr><tr><td>Random forest</td><td>74.1</td><td>73.9</td><td>65.5</td><td>68.4</td><td>71.5</td></tr><tr><td>Stacking ensemble</td><td>74.1</td><td>73.9</td><td>65.5</td><td>68.4</td><td>71.5</td></tr><tr><td>Ensemble</td><td>73.3</td><td>73.1</td><td>65.8</td><td>68.6</td><td>69.4</td></tr><tr><td>Ensemble (XGBoost)</td><td>74.4</td><td>74.3</td><td>65.1</td><td>68.1</td><td>73.0</td></tr><tr><td><strong>XGBoost</strong></td><td><strong>74.4</strong></td><td><strong>74.2</strong></td><td><strong>65.3</strong></td><td><strong>68.2</strong></td><td><strong>72.7</strong></td></tr><tr><td colspan="6">No undersampling</td></tr><tr><td colspan="6">With threshold</td></tr><tr><td>Logistic regression</td><td>73.2</td><td>67.0</td><td>63.7</td><td>66.8</td><td>72.9</td></tr><tr><td>LDA</td><td>73.1</td><td>67.1</td><td>63.8</td><td>66.9</td><td>72.8</td></tr><tr><td>Naive Bayes</td><td>71.3</td><td>66.9</td><td>62.6</td><td>65.8</td><td>74.7</td></tr><tr><td>Random forest</td><td>74.1</td><td>67.6</td><td>65.9</td><td>68.8</td><td>70.6</td></tr><tr><td>Ensemble</td><td>73.3</td><td>67.1</td><td>62.8</td><td>66.0</td><td>74.8</td></tr><tr><td>Ensemble (XGBoost)</td><td>74.4</td><td>67.9</td><td>66.3</td><td>69.1</td><td>70.7</td></tr><tr><td>XGBoost</td><td>74.4</td><td>67.9</td><td>66.1</td><td>69.0</td><td>71.1</td></tr></tbody></table>

<table><caption>**Table 14.** Prediction model results stratified across sex and age groups for PE (F1-w is the weighted F1 score).</caption><thead><tr><th>Models</th><th>AUC</th><th>Accuracy</th><th>F1-w</th><th>Sensitivity</th></tr></thead><tbody><tr><td colspan="5">Sex</td></tr><tr><td colspan="5">Male</td></tr><tr><td>Logistic regression</td><td>71.1</td><td>55.9</td><td>69.9</td><td>77.7</td></tr><tr><td>XGBoost</td><td>76.0</td><td>68.0</td><td>79.1</td><td>73.0</td></tr><tr><td colspan="5">Female</td></tr><tr><td>Logistic regression</td><td>69.3</td><td>73.7</td><td>83.7</td><td>53.4</td></tr><tr><td>XGBoost</td><td>74.3</td><td>77.3</td><td>86.0</td><td>57.2</td></tr><tr><td colspan="5">Age</td></tr><tr><td colspan="5">20-40</td></tr><tr><td>Logistic regression</td><td>79.0</td><td>65.8</td><td>78.0</td><td>81.0</td></tr><tr><td>XGBoost</td><td>78.2</td><td>74.1</td><td>83.5</td><td>68.8</td></tr><tr><td colspan="5">40-60</td></tr><tr><td>Logistic Regression</td><td>65.7</td><td>48.7</td><td>63.2</td><td>75.9</td></tr><tr><td>XGBoost</td><td>74.0</td><td>59.0</td><td>71.9</td><td>78.2</td></tr><tr><td colspan="5">60-80</td></tr><tr><td>Logistic regression</td><td>69.6</td><td>58.6</td><td>72.0</td><td>71.5</td></tr><tr><td>XGBoost</td><td>72.3</td><td>66.1</td><td>77.6</td><td>67.7</td></tr></tbody></table>
<table><caption>**Table 15.** Prediction model results stratified across sex and age groups for death (F1-w is the weighted F1 score).</caption><thead><tr><th>Models</th><th>AUC</th><th>Accuracy</th><th>F1-w</th><th>Sensitivity</th></tr></thead><tbody><tr><td>Sex</td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td></td><td></td><td></td><td></td></tr><tr><td>Logistic regression</td><td>72.4</td><td>64.4</td><td>67.0</td><td>71.2</td></tr><tr><td>XGBoost</td><td>74.0</td><td>64.2</td><td>66.8</td><td>74.6</td></tr><tr><td>Female</td><td></td><td></td><td></td><td></td></tr><tr><td>Logistic regression</td><td>72.9</td><td>67.9</td><td>70.8</td><td>65.1</td></tr><tr><td>XGBoost</td><td>74.0</td><td>66.3</td><td>69.6</td><td>70.4</td></tr><tr><td>Age</td><td></td><td></td><td></td><td></td></tr><tr><td>20-40</td><td></td><td></td><td></td><td></td></tr><tr><td>Logistic regression</td><td>67.8</td><td>93.4</td><td>90.2</td><td>0.0</td></tr><tr><td>XGBoost</td><td>70.2</td><td>93.1</td><td>90.5</td><td>4.2</td></tr><tr><td>40-60</td><td></td><td></td><td></td><td></td></tr><tr><td>Logistic regression</td><td>62.9</td><td>77.8</td><td>76.2</td><td>18.1</td></tr><tr><td>XGBoost</td><td>65.0</td><td>74.3</td><td>75.2</td><td>32.1</td></tr><tr><td>60-80</td><td></td><td></td><td></td><td></td></tr><tr><td>Logistic regression</td><td>61.7</td><td>47.6</td><td>46.4</td><td>85.8</td></tr><tr><td>XGBoost</td><td>63.6</td><td>46.9</td><td>44.9</td><td>89.2</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Feature importance from XGBoost PE prediction model using F1-score gain method (average contribution of each feature to predictive performance).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 6.** XGBoost feature importance with SHAP for PE. The values in the legend being higher or darker colour in the plot correspond to higher values of that feature contributing to the prediction either for stronger positive prediction (more colour points for the feature on the right side of the vertical line) or stronger negative prediction of outcome otherwise.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 7.** Feature importance from XGBoost mortality prediction model using F1-score gain method (average contribution of each feature to predictive performance).</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 8.** XGBoost feature importance with SHAP for mortality. The values in the legend being higher or darker colour in the plot correspond to higher values of that feature contributing to the prediction either for stronger positive prediction (more colour points for the feature on the right side of the vertical line) or stronger negative prediction of outcome otherwise.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Figure 9.** XGBoost feature importance with SHAP for PE (only men). The values in the legend being higher or darker colour in the plot correspond to higher values of that feature contributing to the prediction either for stronger positive prediction (more colour points for the feature on the right side of the vertical line) or stronger negative prediction of outcome otherwise.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Figure 10.** XGBoost feature importance with SHAP for PE (only women). The values in the legend being higher or darker colour in the plot correspond to higher values of that feature contributing to the prediction either for stronger positive prediction (more colour points for the feature on the right side of the vertical line) or stronger negative prediction of outcome otherwise.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure 11.** XGBoost feature importance with SHAP for mortality (only men). The values in the legend being higher or darker colour in the plot correspond to higher values of that feature contributing to the prediction either for stronger positive prediction (more colour points for the feature on the right side of the vertical line) or stronger negative prediction of outcome otherwise.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Figure 12.** XGBoost feature importance with SHAP for mortality (only women). The values in the legend being higher or darker colour in the plot correspond to higher values of that feature contributing to the prediction either for stronger positive prediction (more colour points for the feature on the right side of the vertical line) or stronger negative prediction of outcome otherwise.</figcaption></figure>
<table><caption>**Table 16.** Features of Significant Importance for PE and Mortality Prediction According to Different Models (For XGBoost Top 20 SHAP Value Features Were Taken as Important, and for Logistic Regression and Cox model significance was taken as *p* <0.005). Ticks correspond to significance of feature for that model and for that outcome and X corresponds to lack of significance. Features in bold are those found to be significant for both mortality and PE prediction.</caption><thead><tr><th rowspan="2">Feature</th><th colspan="2">PE</th><th colspan="3">Death</th></tr><tr><th>LR</th><th>XGBoost</th><th>LR</th><th>Cox</th><th>XGBoost</th></tr></thead><tbody><tr><td>Age</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Sex</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Alpha variant</td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Comorbidities</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AIDS/HIV</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Asthma</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Chronic Cardiac Disease</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Chronic Haematological</td><td>X</td><td>X</td><td>✓</td><td>X</td><td>X</td></tr><tr><td><strong>Chronic Kidney Disease</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Chronic Neurological</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Chronic Pulmonary</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td><strong>Dementia</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Diabetes</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td><strong>Hypertension</strong></td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>✓</td></tr><tr><td>Liver Disease</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Malignant Neoplasm</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Malnutrition</td><td>✓</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Obesity</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Rheumatologic</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Smoking</strong></td><td>✓</td><td>✓</td><td>X</td><td>X</td><td>X</td></tr></tbody></table>
<table><caption>**Table 17.** Features of Significant Importance for PE and Mortality Prediction According to Different Models (For XGBoost Top 20 SHAP Value Features Were Taken as Important, and for Logistic Regression and Cox model significance was taken as *p* < 0.005). Ticks correspond to significance of feature for that model and for that outcome and X corresponds to lack of significance. Features in bold are those found to be significant for both mortality and PE prediction. (continued).</caption><thead><tr><th rowspan="2">Feature</th><th colspan="2">PE</th><th colspan="3">Death</th></tr><tr><th>LR</th><th>XGBoost</th><th>LR</th><th>Cox</th><th>XGBoost</th></tr></thead><tbody><tr><td><em>Symptoms</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Symptomatic</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Abdominal Pain</td><td>✓</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Confusion</td><td>✓</td><td>X</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Bleeding</td><td>✓</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Chest Pain</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Conjunctivitis</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Cough</td><td>X</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Diarrhoea</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Ear Pain</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td><strong>Fatigue</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Headache</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td><strong>Fever</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Lost Sense of Smell</td><td>✓</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Lost Sense of Taste</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Lymphadenopathy</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Muscle/Joint Pain</strong></td><td>✓</td><td>X</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Runny Nose</td><td>X</td><td>X</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>Seizures</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Severe Dehydration</td><td>✓</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td><strong>Shortness of Breath</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Skin Rash</td><td>X</td><td>X</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>Sore Throat</td><td>✓</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td><strong>Vomiting</strong></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Wheezing</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>X</td></tr><tr><td>PE</td><td>-</td><td>-</td><td>✓</td><td>✓</td><td>X</td></tr></tbody></table>

<figure><img src="image_15.png" /><figcaption>**Figure 13.** Age distribution for all patients stratified by death outcome.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Figure 14.** Age distribution for UK and Spain patients.</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>**Figure 15.** Flowchart of framework with machine learning model to predict the risk of PE and mortality at admission.</figcaption></figure>
<table><caption>**Table 18.** Machine learning methods investigated.</caption><thead><tr><td>Models</td><td>Brief description</td></tr></thead><tbody><tr><td>Logistic regression</td><td>Generalised linear model</td></tr><tr><td>Linear Discriminant Analysis</td><td>Normal distribution, linear</td></tr><tr><td>Naive Bayes</td><td>Independence, probabilistic</td></tr><tr><td>Random Forest</td><td>Decision tree ensemble</td></tr><tr><td>XGBoost</td><td>Gradient-boosted decision trees</td></tr><tr><td>Ensemble</td><td>AdaBoost models ensemble</td></tr><tr><td>Ensemble with XGBoost</td><td>XGBoost models ensemble</td></tr></tbody></table>

## Data availability

The ISARIC-WHO CCP, case report form and consent forms are openly available on the ISARIC website at https://isaric.org/re search/covid-19-clinical-research-resources/clinical-characterisation-protocol-ccp/. Informed consent for data collection, sharing and/or analysis was obtained from individual participants or their representatives when required by local ethics committees. Some committees approved a waiver of consent due to the public benefit of the research and the minimal risk to participants. The data that underpin this analysis are highly detailed clinical data on individuals hospitalised with COVID-19. Due to the sensitive nature of these data and the associated privacy concerns, they are available via a governed data access mechanism following review of a data access committee. Data can be requested via the IDDO COVID-19 Data Sharing Platform (http://www.iddo.org/covid-19). The Data Access Application, Terms of Access and details of the Data Access Committee are available on the website. Briefly, the requirements for access are a request from a qualified researcher working with a legal entity who have a health and/or research remit; a scientifically valid reason for data access which adheres to appropriate ethical principles. The full terms are at https://www.iddo.org/document/covid-19-data-access-guidelines. A small subset of sites who contributed data to this analysis have not agreed to pooled data sharing as above. In the case of requiring access to these data, please contact the corresponding author in the first instance who will look to facilitate access.

GR declares receiving a grant from United States National Institute of Health, R01 Grant: Emerging Zoonotic Malaria in Malaysia: Strenghtening Surveillance and Evaluating Population Genetics Structure to Improve Regional Risk Prediction Tool and travel support from the European Society of Clinical Microbiology and Infectious Disease (ESCMID) for observership at European Centre for Disease Prevention and Control (ECDC). All authors declare no competing interests.

Received: 12 January 2023; Accepted: 27 May 2024

Published online: 16 July 2024

## References

1. WHO. Novel coronavirus (2019-ncov): situation report, 11. (2020).

2. University, J. H. Covid-19 dashboard by the center for systems science and engineering (csse) (2022).

3. Yang, X. et al. Clinical course and outcomes of critically ill patients with sars-cov-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. *Lancet Respir. Med.* **8**, 475–481 (2020).

4. Liao, S.-C., Shao, S.-C., Chen, Y.-T., Chen, Y.-C. & Hung, M.-J. Incidence and mortality of pulmonary embolism in covid-19: A systematic review and meta-analysis. *Crit. Care* **24**, 1–5 (2020).

5. Knight, S. R. et al. Prospective validation of the 4c prognostic models for adults hospitalised with covid-19 using the isaric who clinical characterisation protocol. *Thorax* **77**, 606–615 (2021).

6. Jones, A. et al. External validation of the 4c mortality score among covid-19 patients admitted to hospital in Ontario, Canada: A retrospective study. *Sci. Rep.* **11**, 1–7 (2021).

7. Tabata, S. et al. Clinical characteristics of covid-19 in 104 people with sars-cov-2 infection on the diamond princess cruise ship: A retrospective analysis. *Lancet. Infect. Dis.* **20**, 1043–1050 (2020).

8. Susen, S. et al. Prevention of thrombotic risk in hospitalized patients with covid-19 and hemostasis monitoring. *Crit. Care* **24**, 1–8 (2020).

9. Whiteley, W. & Wood, A. Risk of arterial and venous thromboses after covid-19. *Lancet Infect. Dis.* **22**, 1093–1094 (2022).

10. Katsoularis, I. et al. Risks of deep vein thrombosis, pulmonary embolism, and bleeding after covid-19: nationwide self-controlled cases series and matched cohort study. *bmj377* (2022).

11. Marcos, M. et al. Development of a severity of disease score and classification model by machine learning for hospitalized covid-19 patients. *PLoS ONE* **16**, e0240200 (2021).

12. Venturini, S. et al. Classification and analysis of outcome predictors in non-critically ill covid-19 patients. *Intern. Med. J.* **51**, 506–514 (2021).

13. Zhou, F. et al. Clinical course and risk factors for mortality of adult inpatients with covid-19 in Wuhan, China: A retrospective cohort study. *The Lancet* **395**, 1054–1062 (2020).

14. Xie, J. et al. Development and external validation of a prognostic multivariable model on admission for hospitalized patients with covid-19. (2020).

15. Alaa, A., Qian, Z., Rashbass, J., Benger, J. & van der Schaar, M. Retrospective cohort study of admission timing and mortality following covid-19 infection in England. *BMJ Open* **10**, e042712 (2020).

16. van de Sande, D. et al. Predicting thromboembolic complications in covid-19 icu patients using machine learning. *J. Clin. Transl. Res.* **6**, 179 (2020).

17. Gómez, C. A. et al. Mortality and risk factors associated with pulmonary embolism in coronavirus disease 2019 patients: A systematic review and meta-analysis. *Sci. Rep.* **11**, 1–13 (2021).
18. Law, N., Chan, J., Kelly, C., Auffermann, W. F. & Dunn, D. P. Incidence of pulmonary embolism in covid-19 infection in the ed: Ancestral, delta, omicron variants and vaccines. *Emerg. Radiol.* **29**, 625–629 (2022).

19. Ikemura, K. et al. Using automated machine learning to predict the mortality of patients with covid-19: Prediction model development study. *J. Med. Internet Res.* **23**, e23458 (2021).

20. Alballa, N. & Al-Turaiki, I. Machine learning approaches in covid-19 diagnosis, mortality, and severity risk prediction: A review. *Inform. Med. Unlock.* **24**, 100564 (2021).

21. Akhvlediani, T. et al. Isaric clinical characterisation group. *Global Outbreak Res. Harmony Not Hegemony Lancet Infect. Dis.* **20**, 770–772 (2020).

22. Kumari, R. & Srivastava, S. K. Machine learning: A review on binary classification. *Int. J. Comput. Appl.* **160** (2017).

23. Chowdhury, M. E. et al. An early warning tool for predicting mortality risk of covid-19 patients using machine learning. *Cognit. Comput.* **1**–16 (2021).

24. Baqui, P. et al. Comparing covid-19 risk factors in brazil using machine learning: The importance of socioeconomic, demographic and structural factors. *Sci. Rep.* **11**, 1–10 (2021).

25. Ling, C. X. & Sheng, V. S. Cost-sensitive learning and the class imbalance problem. *Encycl. Mach. Learn.* **2011**, 231–235 (2008).

26. Lemaître, G., Nogueira, F. & Aridas, C. K. Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. *J. Mach. Learn. Res.* **18**, 559–563 (2017).

27. Liu, T.-Y. Easyensemble and feature selection for imbalance data sets. In *2009 international joint conference on bioinformatics, systems biology and intelligent computing*, 517–520 (IEEE, 2009).

28. Lundberg, S. M. et al. From local explanations to global understanding with explainable ai for trees. *Nat. Mach. Intell.* **2**, 56–67 (2020).

29. Ibrahim, L., Mesinovic, M., Yang, K.-W. & Eid, M. A. Explainable prediction of acute myocardial infarction using machine learning and shapley values. *IEEE Access* **8**, 210410–210417 (2020).

## Acknowledgements

M. Mesinovic appreciates the support of the EPSRC Center for Doctoral Training in Health Data Science (EP/S02428X/1) and the Rhodes Trust.

This work was made possible by the UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z and 220757/Z/20/Z]; the Bill & Melinda Gates Foundation [OPP1209135]; the philanthropic support of the donors to the University of Oxford's COVID-19 Research Response Fund (0009109); CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and the coordination in Canada by Sunnybrook Research Institute; endorsement of the Irish Critical Care-Clinical Trials Group, co-ordination in Ireland by the Irish Critical Care-Clinical Trials Network at University College Dublin and funding by the Health Research Board of Ireland [CTN-2014-12]; the Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589] and European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313]; the COVID clinical management team, AIIMS. Rishikesh, India; the COVID-19 Clinical Management team, Manipal Hospital Whitefield, Bengaluru, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team and supported by the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town; the Liverpool School of Tropical Medicine and the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 study team and the Research Council of Norway grant no 312780, and a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; Imperial NIHR Biomedical Research Centre; the Comprehensive Local Research Networks (CLRNs) of which PJMO is an NIHR Senior Investigator (NIHR201385); Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies, in-kind contribution; Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; Italian Ministry of Health "Fondi Ricerca corrente-L1P6" to IRCCS Ospedale Sacro Cuore-Don Calabria; Australian Department of Health grant (3273191); Gender Equity Strategic Fund at University of Queensland, Artificial Intelligence for Pandemics (A14PAN) at University of Queensland, the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUIS, CE170100009), the Prince Charles Hospital Foundation, Australia; grants from Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7; the Farland Foundation, Shoreline, Washington, USA; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funded by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n20-0424); a grant from foundation Bevordering Onderzoek Franciscus; the South Eastern Norway Health Authority and the Research Council of Norway; Institute for Clinical Research (ICR), National Institutes of Health (NIH) supported by the Ministry of Health Malaysia; preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection; the U.S. DoD Armed Forces Health Surveillance Division, Global Emerging Infectious Diseases Branch to the U.S Naval Medical Research Unit No. TWO (NAMRU-2) (Work Unit #: P0153_21_N2). These authors would like to thank Vysnova Partners, Inc. for the management of this research project. The Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit is funded by the Wellcome Trust.

This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The data used for this research were obtained from ISARIC4C. We are extremely grateful to the 2648 frontline NHS clinical and research staff and volunteer medical students who collected these data in challenging circumstances; and the generosity of the patients and their families for their individual contributions in these difficult times. The COVID-19 Clinical Information Network (CO-CIN) data was collated by ISARIC4C Investigators. Data and Material provision was supported by grants from: the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant
C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award ISBRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. We also acknowledge the support of Jeremy J Farrar and Nahoko Shindo.

## Author contributions

CK conceived and designed the study. The data was curated by LM and BC. Formal analysis was undertaken by MM. Development of the statistical analysis and machine learning methodologies was completed by MM, LC, and CK. Project administration was done by CK, LC, LM, and BC. Software was developed by MM, and validated by MM and CK. Supervision was provided by CK and LC. Visualisations, writing, and editing was done by MM. Resources, clinical or otherwise, were provided by LM, PO, XW, GR, KP, and FG. LM, PO, XW, GR, KP, and FG also undertook the acquisition, analysis, and interpretation of the data. All authors subsequently critically edited the report. The corresponding author and CK had full access to all data. MM and CK accessed and verified the data and results. MM and CK had final responsibility for the decision to submit for publication. All authors have revised, edited, reviewed, and approved all versions of the manuscript. The full list of consortium members is included at the end of the Supplementary material.

## Additional information

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1038/s41598-024-63212-7.

Correspondence and requests for materials should be addressed to M.M.

Reprints and permissions information is available at www.nature.com/reprints.

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_18.png" /></figure>

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024

## ISARIC Characterisation Group

Sheryl Ann Abdukahil<sup>7</sup>, Nurul Najmee Abdulkadir<sup>8</sup>, Ryuzo Abe<sup>9</sup>, Laurent Abel<sup>10</sup>,
Amal Abrous<sup>10</sup>, Lara Absil<sup>11</sup>, Andrew Acker<sup>12</sup>, Shingo Adachi<sup>13</sup>, Elisabeth Adam<sup>14</sup>,
Enrico Adriano<sup>15</sup>, Diana Adrião<sup>16</sup>, Saleh Al Ageel<sup>17</sup>, Shakeel Ahmed<sup>18</sup>, Marina Aiello<sup>15</sup>,
Kate Ainscough<sup>19</sup>, Eka Airlangga<sup>20</sup>, Tharwat Aisa<sup>21</sup>, Ali Ait Hssain<sup>22</sup>, Younes Ait Tamlihat<sup>23</sup>,
Takako Akimoto<sup>24</sup>, Ernita Akmal<sup>25</sup>, Eman Al Qasim<sup>7</sup>, Razi Alalqam<sup>26</sup>, Angela Alberti<sup>27</sup>,
Tala Al-dabbous<sup>28</sup>, Senthilkumar Alegesan<sup>29</sup>, Cynthia Alegre<sup>30</sup>, Marta Alessi<sup>31</sup>,
Beatrice Alex<sup>32</sup>, Kévin Alexandre<sup>33</sup>, Abdulrahman Al-Fares<sup>34</sup>, Huda Alfoudri<sup>28</sup>, Adam Ali<sup>35</sup>,
Imran Ali<sup>36</sup>, Kazali Enagnon Alidjnou<sup>37</sup>, Jeffrey Aliudin<sup>22</sup>, Qabas Alkhafajee<sup>18</sup>,
Clotilde Allavena<sup>38</sup>, Nathalie Allou<sup>39</sup>, João Alves<sup>40</sup>, João Melo Alves<sup>41</sup>, Rita Alves<sup>42</sup>,
Joana Alves Cabrita<sup>42</sup>, Maria Amaral<sup>42</sup>, Nur Amira<sup>43</sup>, Heidi Ammerlaan<sup>44</sup>, Phoebe Ampaw<sup>45</sup>,
Roberto Andini<sup>46</sup>, Claire Andréjak<sup>47</sup>, Andrea Angheben<sup>48</sup>, François Angoulvant<sup>10</sup>,
Séverine Ansart<sup>49</sup>, Sivanesen Anthonidass<sup>50</sup>, Massimo Antonelli<sup>51</sup>,
Carlos Alexandre Antunes de Brito<sup>52</sup>, Kazi Rubayet Anwar<sup>53</sup>, Ardiyan Apriyana<sup>54</sup>,
Yaseen Arabi<sup>55</sup>, Irene Aragao<sup>56</sup>, Francisco Arancibia<sup>57</sup>, Carolline Araujo<sup>52</sup>,
Antonio Arcadipane<sup>58</sup>, Patrick Archambault<sup>59</sup>, Lukas Arenz<sup>60</sup>, Jean-Benoît Arlet<sup>61</sup>,
Christel Arnold-Day<sup>62</sup>, Lovkesh Arora<sup>63</sup>, Rakesh Arora<sup>64</sup>, Elise Artaud-Macari<sup>33</sup>,
Diptesh Aryal<sup>65</sup>, Motohiro Asaki<sup>66</sup>, Angel Asensio<sup>67</sup>, Elizabeth A. Ashley<sup>68</sup>,
Muhammad Ashraf<sup>43</sup>, Jean Baptiste Assie<sup>69</sup>, Amirul Asyraf<sup>43</sup>, Anika Atique<sup>70</sup>,
AM Udara Lakshan Attanyake<sup>65</sup>, Johann Auchabie<sup>71</sup>, Hugues Aumaitre<sup>72</sup>, Adrien Auvet<sup>61</sup>,
Laurène Azemar<sup>73</sup>, Cecile Azoulay<sup>74</sup>, Benjamin Bach<sup>32</sup>, Delphine Bachelet<sup>10</sup>, Claudine Badr<sup>75</sup>,
Nadia Baig<sup>76</sup>, J. Kenneth Baillie<sup>77</sup>, J Kevin Baird<sup>20</sup>, Erica Bak<sup>78</sup>, Agamemnon Bakakos<sup>79</sup>,
Nazreen Abu Bakar<sup>8</sup>, Andriy Bal<sup>80</sup>, Mohanaprasanth Balakrishnan<sup>43</sup>, Valeria Balan<sup>4</sup>,
Firouzé Bani-Sadr<sup>81</sup>, Renata Barbalho<sup>82</sup>, Nicholas Yuri Barbosa<sup>83</sup>, Wendy S. Barclay<sup>84</sup>,
Saef Umar Barnett<sup>43</sup>, Michaela Barnikel<sup>60</sup>, Helena Barrasa<sup>85</sup>, Audrey Barrelet<sup>86</sup>,
Cleide Barrigoto⁴¹, Marie Bartoli¹⁰, Joaquín Baruch⁴, Romain Basmaci¹⁰,
Muhammad Fadhli Hassin Basri⁴³, Denise Battaglini⁸⁷, Jules Bauer³⁷,
Diego Fernando Bautista Rincon⁸⁸, Denisse Bazan Dow⁸⁹, Abigail Beane⁹⁰,
Alexandra Bedossa⁸⁶, Ker Hong Bee⁹¹, Husna Begum⁹², Sylvie Behilill¹⁰,
Albertus Beishuizen⁹³, Aleksandr Beljantsev⁹⁴, David Bellemare⁹⁵, Anna Beltrame⁹⁶,
Beatriz Amorim Beltrão⁹⁷, Marine Beluze¹⁰, Nicolas Benech⁹⁸, Lionel Eric Benjiman⁹⁹,
Dehbia Benkerrou¹⁰, Suzanne Bennett¹⁰⁰, Luís Bento⁴¹, Jan-Erik Berdal¹⁰¹,
Delphine Bergeaud²³, Hazel Bergin¹⁹, Giulia Bertoli¹⁰², Lorenzo Bertolino⁴⁶, Simon Bessis¹⁰³,
Adam Betz¹⁰⁴, Sybille Bevilcaqua¹⁰⁵, Karine Bezulier¹⁰⁶, Amar Bhatt¹⁰⁷, Krishna Bhavsar¹⁰,
Claudia Bianco⁵⁸, Farah Nadiah Bidin⁴³, Moirangthem Bikram Singh¹⁰⁸, Felwa Bin Humaid⁷,
Mohd Nazlin Bin Kamarudin⁴³, Zeno Bisoffi¹⁰⁹, François Bissuel¹¹⁰, Patrick Biston¹¹¹,
Laurent Bitker¹¹², Jonathan Bitton¹¹³, Pablo Blanco-Schweizer¹¹⁴, Catherine Blier⁹⁵,
Frank Bloos¹¹⁵, Mathieu Blot¹¹⁶, Lucille Blumberg¹¹⁷, Filomena Boccia⁴⁶, Laetitia Bodenes⁴⁹,
Debby Bogaert¹¹⁸, Anne-Hélène Boivin¹¹⁹, Isabela Bolaños¹²⁰, Pierre-Adrien Bolze¹¹²,
François Bompart¹⁰, Patrizia Bonelli¹⁵, Aurelius Bonfasius¹²¹, Diogo Borges⁴²,
Raphaël Borie¹²², Hans Martin Bosse¹²³, Elisabeth Botelho-Nevers¹²⁴, Lila Bouadma¹⁰,
Olivier Bouchaud¹²⁵, Sabelline Bouchez³⁸, Dounia Bouhmani¹²⁶, Damien Bouhour¹²⁷,
Kévin Bouiller¹²⁸, Laurence Bouillet¹²⁹, Camile Bouisse¹²⁷, Thipsavanh Bounphiengsy¹³⁰,
Latsaniphone Bountthasavong¹³¹, Anne-Sophie Boureau¹³², John Bourke²⁹,
Maude Bouscambert¹⁰, Aurore Bousquet¹³³, Jason Bouziotis¹¹, Bianca Boxma¹³⁴,
Marielle Boyer-Besseyre³⁷, Maria Boylan²⁹, Fernando Augusto Bozza¹³⁵, Axelle Braconnier¹³⁶,
Cynthia Braga⁵², Timo Brandenburger¹²³, Filipa Brás Monteiro¹³⁷, Luca Brazzi¹³⁸,
Dorothy Breen¹³⁹, Patrick Breen¹⁴⁰, David Brewster¹⁴¹, Kathy Brickell¹⁹, Alex Browne¹⁴²,
Shaunagh Browne¹³⁹, Nicolas Brozzi¹⁴³, Marjolein Brusse-Keizer⁹³, Nina Buchtele¹⁴⁴,
Polina Bugaeva¹⁴⁵, Marielle Buisson¹¹⁶, Danilo Buonsenso⁹⁶, Erlina Burhan²⁵, Aidan Burrell¹⁴¹,
Ingrid G. Bustos¹⁴⁶, Denis Butnaru¹⁴⁵, André Cabie¹⁴⁷, Susana Cabral⁴⁰, Eder Caceres¹⁴⁶,
Cyril Cadoz¹⁴⁸, Rui Caetano Garcês⁴², Mia Callahan¹⁴⁹, Kate Calligy¹⁵⁰,
Jose Andres Calvache¹²⁰, Caterina Caminiti¹⁵, João Camões¹⁵¹, Valentine Campana¹⁴⁷,
Paul Campbell¹⁵², Josie Campisi⁷⁰, Cecilia Canepa¹⁵³, Mireia Cantero⁶⁷, Pauline Caraux-Paz⁷⁵,
Sheila Cárcel¹⁵⁴, Chiara Simona Cardellino¹⁰², Filipa Cardoso⁴², Filipe Cardoso⁴²,
Nelson Cardoso¹⁵⁵, Sofia Cardoso⁴², Simone Carelli⁵¹, Francesca Caracci¹⁵, Nicolas Carlier⁷⁴,
Thierry Carmoi¹⁵⁶, Gayle Carney¹⁵⁷, Inês Carqueja¹⁵¹, Marie-Christine Carret¹⁵⁸,
François Martin Carrier¹⁵⁹, Ida Carroll¹⁶⁰, Gail Carson⁴, Leonor Carvalho¹⁶¹,
Maire-Laure Casanova¹⁶², Mariana Cascão⁴², Siobhan Casey²⁹, José Casimiro⁴²,
Bailey Cassandra¹⁰⁰, Silvia Castañeda¹⁵³, Nidyanara Castanheira⁸²,
Guylaine Castor-Alexandre⁷³, Ivo Castro¹⁶³, Ana Catarino⁴⁰, François-Xavier Catherine¹¹⁶,
Paolo Cattaneo¹⁰², Roberta Cavalin⁸², Giulio Giovanni Cavalli¹⁶⁴, Alexandros Cavayas¹⁶⁵,
Adrian Ceccato¹⁶⁶, Shelby Cerkovnik¹⁶⁷, Minerva Cervantes-Gonzalez¹⁰, Anissa Chair¹⁰,
Catherine Chakveatze¹⁶⁸, Bounthavy Chaleunphon¹⁶⁹, Adrienne Chan¹⁷⁰, Meera Chand¹⁷¹,
Christelle Chantalat Auger¹⁷², Jean-Marc Charplain¹⁷³, Charlotte Charpentier¹²², Julie Chas¹⁷⁴,
Allegra Chatterjee³⁵, Jonathan Samuel Chávez Iñiguez¹⁷⁵, Anjellica Chen⁷⁰, Léo Chenard¹²²,
Matthew Pellan Cheng⁷⁰, Antoine Cheret¹⁷², Alfredo Antonio Chetta¹⁵, Thibault Chiarabini¹⁷⁶,
Julian Chica⁸⁸, Suresh Kumar Chidamaram¹⁷⁷, Leong Chin Tho⁵⁰, Catherine Chirouze¹²⁸,
Davide Chiumello¹⁷⁸, Hwa Jin Cho¹⁷⁹, Sung-Min Cho¹⁵⁰, Bernard Cholley⁶¹,
Danoy Chommanam¹⁸⁰, Marie-Charlotte Chopin³⁷, Ting Soo Chow¹⁸¹, Yock Ping Chow¹⁸²,
Nathaniel Christy¹⁸⁰, Hiu Jian Chua¹⁷⁷, Jonathan Chua⁴³, José Pedro Cidade¹⁶³,
José Miguel Cisneros Herreros¹⁸³, Barbara Wanjiru Citarella⁴, Anna Ciullo¹⁸⁴, Emma Clarke¹⁸⁵,
Jennifer Clarke²⁶, Rolando Claure-Del Granado⁸³, Sara Clohisey¹⁸⁶, Cassidy Codan¹⁸⁷,
Caitriona Cody¹⁸⁸, Alexandra Coelho¹⁰, Jennifer Coles¹⁴¹, Megan Coles¹⁸⁹, Gwenhaël Colin¹⁹⁰,
Michael Collins¹⁹¹, Sebastiano Maria Colombo¹⁹², Pamela Combs¹⁹³, Marie Connor¹⁹⁴,
Anne Conrad⁹⁸, Sofia Contreras³⁰, Elaine Conway¹⁶⁰, Graham S. Cooke¹⁹⁵, Mary Copland¹⁹⁶,
Hugues Cordel¹²⁵, Amanda Corley¹⁹⁷, Sabine Cornelis¹¹, Alexander Daniel Cornet⁹³,
Arianne Joy Corpuz²⁸, Andrea Cortegiani³¹, Grégory Corvaisier¹⁹⁸, Emma Costigan¹⁸⁵,
Camille Couffignal¹⁰, Sandrine Couffin-Cadiergues¹⁰, Roxane Courtois⁷¹, Stéphanie Cousse¹⁹⁹,
Rachel Cregan¹⁸⁵, Charles Crepy D'Orleans¹⁴⁹, Cosimo Cristella²⁰⁰, Sabine Croonen²⁰¹,
Gloria Crowl²⁰², Jonathan Crump¹⁸⁴, Claudina Cruz²⁰³, Marc Csete²⁰⁴, Ailbhe Cullen²⁶,
Matthew Cummings²⁰⁵, Ger Curley²⁶, Elodie Curlier²⁰⁶, Colleen Curran¹³⁹, Paula Custodio⁸²,
Ana da Silva Filipe²⁰⁷, Charlene Da Silveira¹⁰, Al-Awwab Dabaliz²⁰⁸, Andrew Dagens⁴,
Darren Dahly²⁰⁹, Heidi Dalton¹⁹⁷, Jo Dalton¹⁹⁴, Seamus Daly²⁹, Juliana Damas⁴²,
Nick Daneman¹⁷⁰, Corinne Daniel²¹⁰, Emmanuelle A Dankwa²¹¹, Jorge Dantas⁴²,
Frédérick D'Aragon²¹², Menno de Jong²⁰⁰, Gillian de Loughry²⁰⁹, Diego de Mendoza¹⁶⁶,
Etienne De Montmollin¹²², Rafael Freitas de Oliveira França⁵², Ana Isabel de Pinho Oliveira²¹³,
Rosanna De Rosa²¹⁴, Cristina De Rose²¹⁵, Thushan de Silva²¹⁶, Peter de Vries²⁰¹,
Jillian Deacon²¹⁷, David Dean²¹⁸, Alexa Debard²¹⁹, Marie-Pierre Debray¹⁰,
Nathalie DeCastro²²⁰, William Dechert¹⁹⁶, Lauren Deconninck¹²², Romain Decours¹⁹⁰,
Eve Defous²²¹, Isabelle Delacroix⁶⁹, Eric Delaveuve²²², Karen Delavigne²²³,
Nathalie M. Delfos²²⁴, Ionna Deligiannis¹⁴, Andrea Dell'Amore²²⁵, Christelle Delmas¹⁰,
Pierre Delobel²¹⁹, Corine Delsing⁹³, Elisa Demonchy²²⁶, Emmanuelle Denis²²⁷,
Dominique Deplanque¹⁰, Pieter Depuydt²²⁸, Mehul Desai²²⁹, Diane Descamps¹⁰,
Mathilde Desvallées¹⁰, Santi Dewayanti⁵⁴, Pathik Dhanger¹⁰⁸, Alpha Diallo¹⁰,
Sylvain Diamantis¹⁶⁸, André Dias²⁰³, Andrea Dias¹⁶¹, Fernanda Dias Da Silva¹⁰,
Juan Jose Diaz²³⁰, Priscila Diaz²³¹, Rodrigo Diaz²³², Kévin Didier⁸¹, Jean-Luc Diehl⁶¹,
Wim Dieperink²³³, Jérôme Dimet²³⁴, Vincent Dinot¹⁴⁸, Fara Diop⁷⁵, Alphonsine Diouf¹⁰,
Yael Dishon²³⁵, Félix Djossou²³⁶, Annemarie B. Docherty²³⁷, Helen Doherty²⁶,
Arjen M Dondorp⁶⁵, Andy Dong¹⁴⁹, Christl A. Donnelly²¹¹, Maria Donnelly²³⁸,
Chloe Donohue¹⁹⁴, Sean Donohue²⁹, Yoann Donohue²⁹, Peter Doran¹⁹, Céline Dorival¹⁰,
Eric D'Ortenzio¹⁰, Phouvieng Douangdala¹³⁰, James Joshua Douglas²³⁹, Renee Douma²⁴⁰,
Nathalie Dournon¹²⁵, Triona Downer²⁴¹, Joanne Downey¹⁹, Mark Downing²⁴², Tom Drake²³⁷,
Aoife Driscoll¹⁶⁰, Murray Dryden¹¹⁷, Claudio Duarte Fonseca⁵², Vincent Dubee²⁴³,
François Dubos¹⁰, Audrey Dubot-Pérès¹⁸⁰, Alexandre Ducancelle²⁴³, Toni Duculan²⁴⁴,
Susanne Dudman²⁴⁵, Abhijit Duggal²⁴⁶, Paul Dunand²²², Jake Dunning⁴, Mathilde Duplaix¹⁶⁵,
Emanuele Durante-Mangoni⁴⁶, Lucian Durham III²⁴⁷, Bertrand Dussol²⁴⁸, Juliette Duthoit²⁴⁹,
Xavier Duval¹⁰, Anne Margarita Dyrhol-Riise²⁴⁵, Sim Choon Ean¹⁸¹,
Marco Echeverria-Villalobos¹⁰⁷, Giorgio Economopoulos¹⁵, Siobhan Egan¹⁶⁰, Carla Eira²¹³,
Mohamed El Sanharawi²⁵⁰, Subbarao Elapavaluru¹⁹¹, Brigitte Elharrar⁶⁹,
Jacobien Ellerbroek²⁵¹, Philippine Eloy¹⁰, Tarek Elshazly²⁰⁸, Iqbal Elyazar¹²¹,
Isabelle Enderle²⁵², Tomoyuki Endo²⁵³, Chan Chee Eng⁵⁰, Ilka Engelmann³⁷, Vincent Enouf¹⁰,
Olivier Epaulard¹²⁹, Martina Escher⁴, Mariano Esperatti²⁵⁴, Hélène Esperou¹⁰,
Catarina Espírito Santo⁴², Marina Esposito-Farese¹⁰, João Estevão⁴², Manuel Etienne¹⁰,
Nadia Ettalhaoui¹⁰, Anna Greti Everding²⁵⁵, Mirjam Evers²⁵⁶, Isabelle Fabre²⁰⁶, Marc Fabre²⁵⁷,
Amna Faheem²⁵⁸, Arabella Fahy²³⁸, Cameron J. Fairfield²³⁷, Zul Fakar²⁵⁹, Pedro Faria⁴²,
Hanan Fateena⁴³, Arie Zainul Fatoni²⁶⁰, Karine Faure³⁷, Raphaël Favory³⁷,
Mohamed Fayed²⁶¹, Niamh Feely¹⁸, Laura Feeney¹⁹, Jorge Fernandes⁴²,
Marília Andreia Fernandes⁴², Susana Fernandes²⁶², François-Xavier Ferrand²⁶³,
Eglantine Ferrand Devouge³³, Joana Ferrão⁴², Carlo Ferrari¹⁵, Mário Ferraz⁴²,
Benigno Ferreira²⁵⁵, Bernardo Ferreira⁴², Isabel Ferreira²³¹, Sílvia Ferreira¹⁵¹,
Richard Ferrer-Roca³⁰, Nicolas Ferriere⁴⁹, Céline Ficko¹³³, Claudia Figueiredo-Mello⁸²,
William Finlayson³⁵, Juan Fiorda¹⁰⁷, Thomas Flament²⁶⁴, Clara Flateau¹⁶⁸, Tom Fletcher²⁶⁵,
Aline-Marie Florence¹²², Letizia Lucia Florio⁴⁶, Brigid Flynn²⁶⁶, Deirdre Flynn¹³⁹,
Federica Fogliazza¹⁵, Claire Foley²⁶, Jean Foley¹³⁹, Victor Fomin¹⁴⁵, Tatiana Fonseca¹⁶,
Patricia Fontela²⁶⁷, Simon Forsyth¹⁹⁷, Denise Foster²⁶⁸, Giuseppe Foti²⁶⁹, Erwan Fourn²⁷⁰,
Robert A. Fowler²⁷¹, Marianne Fraher²⁷², Diego Franch-Llasat²⁷³, Christophe Fraser²²⁷,
John F Fraser¹⁹⁷, Marcela Vieira Freire⁵², Ana Freitas Ribeiro⁸², Caren Friedrich²⁷⁴,
Ricardo Fritz⁵⁷, Stéphanie Fry²⁷⁵, Nora Fuentes²⁵⁴, Masahiro Fukuda²⁷⁶, Argin G²⁷⁷,
Valérie Gaborieau²⁷⁸, Rostane Gaci¹⁴⁸, Massimo Gagliardi⁴⁶, Jean-Charles Gagnard²⁷⁹,
Amandine Gagneux-Brunon¹²⁴, Sérgio Gaião²⁸⁰, Linda Gail Skeie²⁴⁵, Phil Gallagher¹⁹,
Elena Gallego Curto²⁸¹, Carrol Gamble¹⁹⁴, Yasmin Gani⁴³, Arthur Garan²⁸², Rebekha Garcia²⁶¹,
Julia Garcia-Diaz²⁸³, Esteban Garcia-Gallo¹⁴⁶, Navya Garimella⁸⁹, Federica Garofalo¹⁵,
Denis Garot²⁶⁴, Valérie Garrait⁶⁹, Nathalie Gault¹⁰, Aisling Gavin²⁴¹, Anatoliy Gavrylov²⁸⁴,
Alexandre Gaynard¹⁰, Johannes Gebauer²⁸⁵, Eva Geraud²²¹, Louis Gerbaud Morlaes²³⁴,
Nuno Germano⁴², praveen kumar ghisulal²⁸⁶, Jade Ghosn¹⁰, Marco Giani²⁶⁹,
Carlo Giaquinto²⁸⁷, Jess Gibson²⁸⁸, Tristan Gigante¹⁰, Morgane Gilg¹⁰, Elaine Gilroy²⁸⁹,
Guillermo Giordano²⁹⁰, Michelle Girvan¹⁹⁴, Valérie Gissot²⁶⁴, Daniel Glikman²⁹¹,
Petr Glybochko¹⁴⁵, Eric Gnall²⁹², Geraldine Goco²⁹³, François Goehringer¹⁰⁵, Siri Goepel²⁹⁴,
Jean-Christophe Goffard²⁹⁵, Jin Yi Goh²⁹⁶, Jonathan Golob²⁹⁷, Rui Gomes²⁹⁸, Kyle Gomez³⁵,
Joan Gómez-Junyent²⁹⁹, Marie Gominet¹³³, Alicia Gonzalez¹⁰⁷, Patricia Gordon³⁰⁰,
Isabelle Gorenne¹⁰, Laure Goubert¹⁹⁹, Cécile Goujard¹⁷², Tiphaine Goulenok¹²²,
Margarite Grable³⁰¹, Jeronimo Graf³⁰², Edward Wilson Grandin²⁸², Pascal Granier³⁰³,
Giacomo Grasselli¹⁹², Christopher A. Green³⁰⁴, Courtney Greene¹⁸⁵, William Greenhalf³⁰⁵,
Segolène Greffe³⁰⁶, Domenico Luca Grieco⁵¹, Matthew Griffee¹⁸⁴, Fiona Griffiths³⁰⁷,
Ioana Grigoras³⁰⁸, Albert Groenendijk³⁰⁹, Anja Grosse Lordemann³¹⁰, Heidi Gruner³¹¹,
Yusing Gu⁷⁰, Jérémie Guedj¹⁰, Martin Guego²⁶³, Dewi Guellec⁴⁹, Anne-Marie Guerguerian²⁹³,
Daniela Guerreiro⁴², Romain Guery³⁸, Anne Guillaumot¹⁰⁵, Laurent Guilleminault³¹²,
Maisa Guimarães de Castro³¹³, Thomas Guimard¹⁹⁰, Marieke Haalboom⁹³, Daniel Haber²³⁵,
Hannah Habraken³¹⁴, Ali Hachemi³¹⁵, Nadir Hadri¹⁷², Sheeba Hakak²⁰⁹, Adam Hall³¹⁶,
Matthew Hall³¹⁷, Sophie Halpin¹⁹⁴, Ansley Hamer³¹⁸, Rebecca Hamidfar¹²⁹,
Naomi Hammond¹⁴¹, Terese Hammond³¹⁹, Lim Yuen Han³²⁰, Rashan Haniffa⁶⁵,
Kok Wei Hao³²⁰, Hayley Hardwick³²¹, Ewen M. Harrison²³⁷, Janet Harrison¹⁹⁴,
Samuel Bernard Ekow Harrison³²², Alan Hartman²⁷, Junaid Hashmi²¹, Ailbhe Hayes³⁶,
Leanne Hays¹⁹, Jan Heerman³²³, Lars Heggelund³²⁴, Ross Hendry³⁰⁷, Martina Hennessy²⁴¹,
Aquiles Henriquez-Trujillo³²⁵, Maxime Hentzien⁸¹, Diana Hernandez¹²⁰,
Jaime Hernandez-Montfort¹⁴³, Andrew Hershey³²⁶, Liv Hesstvedt²⁴⁵, Astarini Hidayah³²⁷,
Dawn Higgins²⁴¹, Eibhlin Higgins²⁹, Rupert Higgins²²⁷, Rita Hinchion¹⁶⁰, Samuel Hinton¹⁹⁷,
Hiroaki Hiraiwa³²⁸, Haider Hirkani³²⁹, Hikombo Hitoto³³⁰, Antonia Ho³³¹, Yi Bin Ho³³²,
Alexandre Hoctin¹⁰, Isabelle Hoffmann¹⁰, Wei Han Hoh³³³, Oscar Hoiting²⁵⁶, Rebecca Holt³³⁴,
Jan Cato Holter²⁴⁵, Peter Horby⁴, Juan Pablo Horcajada¹⁵³, Koji Hoshino³³⁵, Kota Hoshino³³⁶,
Ikram Houas¹⁰, Catherine L. Hough³³⁷, Stuart Houltham²⁴⁶, Jimmy Ming-Yang Hsu⁷⁰,
Jean-Sébastien Hulot¹⁰, Stella Huo³³⁸, Abby Hurd³³⁹, Samreen Ijaz³⁴⁰, M. Arfan Ikram³⁰⁹,
Carlos Cañada Illana³⁴¹, Hajnal-Gabriela Illes²³⁴, Patrick Imbert³⁴², Hugo Inácio⁴²,
Carmen Infante Dominguez¹⁸³, Yun Sii Ing⁴³, Elias Iosifidis³⁴³, Mariachiara Ippolito³¹,
Vera Irawany³⁴⁴, Sarah Isgett³¹⁸, Tiago Isidoro⁴², Nadiah Ismail⁵⁰, Margaux Isnard¹⁵⁸,
Junji Itai³⁴⁵, Asami Ito³⁴⁶, Daniel Ivulich³⁴⁷, Danielle Jaafar⁷⁵, Salma Jaafoura¹⁰, Julien Jabot³⁹,
Clare Jackson¹⁹⁴, Nina Jamieson³⁴⁸, Victoria Janes²⁰⁰, Pierre Jaquet¹²², Waasila Jassat¹¹⁷,
Coline Jaud-Fischer²²², Stéphane Jaureguiberry¹⁷², Jeffrey Javidfar¹⁴⁹, Denise Jaworsky³⁴⁹,
Florence Jego¹⁵⁸, Anilawati Mat Jelani³⁵⁰, Synne Jenum²⁴⁵, Ruth Jimbo-Sotomayor³²⁵,
Ong Yiaw Joe⁵⁰, Ruth N. Jorge García³⁵¹, Cédric Joseph⁴⁷, Mark Joseph¹⁸⁹, Swosti Joshi²¹⁷,
Mercé Jourdain³⁷, Philippe Jouvet¹¹³, Anna Jung³¹⁹, Hanna Jung³⁵², Dafsah Juzar⁵⁴,
Ouifiya Kafif¹⁰, Florentia Kaguelidou¹⁰, Neerusha Kaisbain²⁹⁶, Thavamany Kaleesvran⁸,
Sabina Kali¹⁰, Alina Kalicinska³⁵³, Smaragdi Kalomoiri⁷⁹,
Muhammad Aisar Ayadi Kamaluddin⁴³, Zul Amali Che Kamaruddin³⁵⁴, Nadiah Kamarudin³⁵⁵,
Darshana Hewa Kandamby³⁵⁶, Chris Kandel²⁰², Kong Yeow Kang¹⁸¹, Pratap Karpayah⁴³,
Todd Karsies³⁵⁷, Christiana Kartsonaki⁶, Daisuke Kasugai³²⁸, Anant Kataria¹⁰⁸, Kevin Katz²⁵⁸,
Aasmine Kaur⁷⁰, Christy Kay³⁵⁸, Hannah Keane¹⁹, Seán Keating³⁵⁹, Pulak Kedia³²⁹,
Andrea Kelly²⁴¹, Aoife Kelly²⁸⁹, Claire Kelly²⁹, Niamh Kelly¹³⁹, Sadie Kelly²²⁷, Yvelynne Kelly²³⁸,
Maeve Kelsey¹³⁹, Ryan Kennedy³⁶⁰, Kalynn Kennon²²⁷, Sommay Keomany¹⁸⁰,
Maeve Kernan²⁹, Younes Kerroumi³⁶¹, Sharma Keshav³⁰⁰, Evelyne Kestelyn³⁶²,
Imrana Khalid³⁶³, Antoine Khalil¹⁰, Coralie Khan¹⁰, Irfan Khan¹⁵², Krish Kherajani³²⁹,
Michelle E Kho³⁶⁴, Denisa Khoo⁴³, Ryan Khoo³⁶⁵, Saye Khoo³⁶⁶, Khor How Kiat³²⁰,
Yuri Kida¹⁸⁴, Peter Kiiza¹⁷⁰, Beathe Kiland Granerud²⁴⁵, Anders Benjamin Kildal³⁶⁷,
Jae Burm Kim³⁶⁸, Antoine Kimmoun¹⁰⁵, Detlef Kindgen-Milles¹²³, Alexander King²⁴⁶,
Nobuya Kitamura³⁶⁹, Paul Klenerman³⁷⁰, Rob Klont⁹³, Gry Kloumann Bekken³²⁴,
Stephen R Knight²³⁷, Robin Kobbe³¹⁰, Chamira Kodippily⁶⁵, Malte Kohns Vasconcelos¹²³,
Mamoru Komatsu³⁷¹, ISARIC Collaborator Korten³⁷², Caroline Kosgei²⁴¹, Arsène Kpangon²³⁶,
Karolina Krawczyk¹⁹, Sudhir Krishnan²⁴⁶, Vinothini Krishnan⁴³, Oksana Kruglova²⁸⁴,
Deepali Kumar³⁷³, Ganesh Kumar⁵⁰, Pavan Kumar Vecham³⁷⁴, Dinesh Kuriakose²¹,
Ethan Kurtzman³⁷⁵, Neurinda Permata Kusumastuti³⁷⁶, Demetrios Kutsogiannis³⁷⁷,
Galyna Kutsyna²⁸⁴, Konstantinos Kyriakoulis⁷⁹, Raph L. Hamers²⁰, Marie Lachatre⁷⁴,
Marie Lacoste³⁷⁸, John G. Laffey²⁹, Nadhem Lafhej¹²², Marie Lagrange³⁹, Fabrice Laine¹⁷³,
Olivier Lairez³⁷⁹, Marc Lambert²⁷⁵, François Lamontagne²¹², Marie Langelot-Richard¹⁹⁸,
Vincent Langlois¹⁹⁹, Eka Yudha Lantang³⁸⁰, Marina Lanza⁸², Cédric Laouénan³⁸¹,
Samira Laribi¹⁰, Delphine Lariviere¹⁹⁸, Stéphane Lasry¹⁵⁶, Sakshi Lath³²⁹, Odile Launay⁷⁴,
Didier Laureillard³⁸², Yoan Lavie-Badie³⁷⁹, Andrew Law³⁸³, Cassie Lawrence³⁸⁴, Teresa Lawrence³⁸⁵, Minh Le¹⁰, Clément Le Bihan³⁸⁶, Cyril Le Bris¹⁶², Georges Le Falher¹⁶², Lucie Le Fevre¹²², Quentin Le Hingrat¹⁰, Marion Le Maréchal¹²⁹, Soizic Le Mestre¹⁰, Gwenaël Le Moal³⁸⁷, Vincent Le Moing³⁸⁶, Hervé Le Nagard¹⁰, Paul Le Turnier³⁸, Ema Leal²⁰³, Marta Leal Santos²⁰³, Biing Horng Lee⁹¹, Heng Gee Lee³⁸⁸, James Lee²²⁷, Jennifer Lee²²⁷, Su Hwan Lee³⁸⁹, Todd C. Lee⁷⁰, Yi Lin Lee⁴³, Gary Leeming³⁹⁰, Bénédicte Lefebvre¹⁷⁶, Laurent Lefebvre³⁰³, Benjamin Lefèvre¹⁰⁵, Sylvie LeGac¹²², Jean-Daniel Lelievre³⁹¹, François Lellouche³⁹², Adrien Lemaignen²⁶⁴, Véronique Lemee³³, Anthony Lemeur⁷¹, Gretchen Lemmink¹⁰⁰, Ha Sha Lene³⁹³, Jenny Lennon¹⁸⁵, Rafael León¹⁵⁴, Marc Leone¹⁰⁶, Michela Leone¹⁹², François-Xavier Lescure¹⁰, Olivier Lesens³⁹⁴, Mathieu Lesouhaitier¹⁷³, Amy Lester-Grant¹⁹, Andrew Letizia¹⁸⁰, Sophie Letrou¹²², Bruno Levy¹⁰⁵, Yves Levy¹⁰, Claire Levy-Marchal¹⁰, Katarzyna Lewandowska³⁹⁵, Erwan L'Her⁴⁹, Gianluigi Li Bassi¹⁹⁷, Janet Liang³⁹⁶, Geoffrey Liegeon²²⁰, Kah Chuan Lim⁴³, Wei Shen Lim³⁹⁷, Chantre Lima¹⁵⁵, Bruno Lina¹⁰, Lim Lina³⁵⁵, Andreas Lind²⁴⁵, Maja Katherine Lingad³⁹⁸, Guillaume Lingas¹⁰, Sylvie Lion-Daolio⁴⁷, Samantha Lissauer³⁹⁹, Keibun Liu⁴⁰⁰, Marine Livrozet⁶¹, Patricia Lizotte³⁹², Antonio Loforte¹⁶⁴, Navy Lolong²⁵, Leong Chee Loon⁵⁰, Diogo Lopes⁴², Dalia Lopez-Colon⁴⁰¹, Jose W. Lopez-Revilla⁴⁰², Anthony L. Loschner¹⁸⁹, Paul Loubet³⁸², Bouchra Loufti²³⁴, Guillame Louis¹⁴⁸, Silvia Lourenco¹⁵⁵, Lara Lovelace-Macon⁸⁹, Lee Lee Low³³², Marije Lowik⁹³, Jia Shyi Loy³⁵⁵, Jean Christophe Lucet¹⁰, Carlos M. Luna⁴⁰³, Olguta Lungu³⁰⁸, Liem Luong⁷⁴, Nestor Luque⁴⁰⁴, Dominique Luton¹²², Nilar Lwin⁴⁰⁵, Ruth Lyons³⁴⁸, Olavi Maasikas⁹⁴, Oryane Mabiala³⁶¹, Moïse Machado⁸⁶, Sara Machado⁴², Gabriel Macheda³⁴², Hashmi Madiha⁶⁵, Giuseppe Maglietta¹⁵, Rafael Mahieu²⁴³, Sophie Mahy¹¹⁶, Ana Raquel Maia²³¹, Lars S. Maier⁴⁰⁶, Mylène Maillet³⁴², Thomas Maitre⁶⁹, Maria Majori¹⁵, Maximilian Malfertheiner⁴⁰⁶, Nadia Malik⁴⁰⁷, Paddy Mallon¹⁹, Fernando Maltez²⁰³, Denis Malvy¹⁰, Patrizia Mammi¹⁵, Victoria Manda⁷³, Jose M. Mandei⁴⁰⁸, Laurent Mandelbrot⁴⁰⁹, Frank Manetta²⁷, Julie Mankikian²⁶⁴, Edmund Manning¹³⁹, Aldric Manuel³⁴², Ceila Maria Sant'Ana Malaque⁸², Daniel Marino²¹⁷, Flávio Marino⁴¹⁰, Samuel Markowicz²⁰⁶, Ana Marques⁴⁰, Catherine Marquis²¹², Brian Marsh²⁸⁹, Megan Marshal¹⁸⁸, John Marshall³⁶³, Celina Turchi Martelli⁵², Dori-Ann Martin⁴¹¹, Emily Martin²⁹⁷, Guillaume Martin-Blondel²¹⁹, Ignacio Martin-Loeches⁴¹², Martin Martinot⁴¹³, Alejandro Martin-Quiros⁴¹⁴, F. Eduardo Martinez¹⁴¹, Ana Martins⁴², João Martins⁴⁰, Nuno Martins²³¹, Caroline Martins Rego⁸², Gennaro Martucci⁵⁸, Olga Martynenko²⁸⁴, Eva Miranda Marwali⁵⁴, Marsilla Marzukie³⁵⁴, Juan Fernado Masa Jimenez²⁸¹, David Maslove⁴¹⁵, Sabina Mason²³⁸, Basri Mat Nor⁴¹⁶, Moshe Matan²⁹¹, Daniel Mathieu³⁷, Mathieu Mattei¹⁰⁵, Romans Matulevics³⁴⁸, Laurence Maulin³⁰³, Michael Maxwell²⁹, Javier Maynar⁸⁵, Mayfong Mayxay¹⁸⁰, Thierry Mazzoni⁷¹, Lisa Mc Sweeney¹³⁹, Peter McCanny¹⁴¹, Colin McArthur⁴¹⁷, Aine McCarthy²⁹, Anne McCarthy⁴⁵, Colin McCloskey²⁰⁸, Rachael McConnochie⁴¹⁸, Sherry McDermott²⁹², Sarah E. McDonald²⁰⁷, Aine McElroy²⁸⁹, Samuel McElwee³³⁴, Victoria McEneany¹⁴⁰, Natalie McEvoy²⁶, Allison McGeer⁴¹⁹, Chris McKay⁴²⁰, Johnny McKeown¹⁰⁷, Kenneth A. McLean²³⁷, Paul McNally¹⁸⁵, Bairbre McNicholas²⁹, Elaine McPartlan¹⁹, Edel Meaney¹⁹, Cécile Mear-Passard³⁸, Maggie Mechlin¹⁰⁰, Omar Mehkri²⁶⁴, Ferruccio Mele⁴⁶, Luis Melo²³¹, Joao Joao Mendes²³¹, Ogechukwu Menkiti²¹⁷, Kusum Menon²⁸⁸, France Mentré³⁸¹, Alexander J. Mentzer⁴²¹, Emmanuelle Mercier²⁶⁴, Noémie Mercier¹⁰, Antoine Merckx⁴²², Mayka Mergeay-Fabre²³⁶, Blake Mergler¹², Laura Merson⁴, Tiziana Meschi¹⁵, António Mesquita⁴², Roberta Meta³⁵, Osama Metwally²⁹, Agnès Meybeck²⁴⁹, Dan Meyer³⁰¹, Alison M. Meynert⁴²³, Vanina Meysonnier³⁶¹, Amina Meziane¹⁰, Mehdi Mezidi¹¹², Céline Michelanglei²²⁶, Isabelle Michelet⁴²⁴, Efstathia Mihelis²⁷, Vladislav Mihnovit⁹⁴, Jennene Miller¹⁴¹, Hugo Miranda-Maldonado³²⁵, Nor Arisah Misnan⁴³, Nik Nur Eliza Mohamed⁵⁰, Tahira Jamal Mohamed⁵⁰, Asma Moin⁴⁰⁷, Elena Molinos¹⁵¹, Brenda Molloy¹⁹, Mary Mone¹⁸⁴, Agostinho Monteiro⁴², Claudia Montes⁸⁸, Giorgia Montrucchio¹³⁸, Sarah Moore²²⁷, Shona C. Moore⁴²⁵, Lina Morales Cely¹⁴⁶, Lucia Moro¹⁰², Ben Morton²⁶⁵, Catherine Motherway¹⁶⁰, Ana Motos⁴²⁶, Hugo Mouquet¹⁰, Clara Mouton Perrot²⁹⁰, Julien Moyet⁴⁷, Caroline Mudara¹¹⁷, Ng Yong Muh⁵⁰, Dzawani Muhamad⁴²⁷, Jimmy Mullaert¹⁰, Fredrik Müller²⁴⁵, Karl Erik Müller³²⁴, Daniel Munblit⁴²⁸, Aisling Murphy²⁰⁹, Lorna Murphy¹⁹, Marlène Murris³¹², Srinivas Murthy⁴²⁹, Himed Musaab³⁰⁰, Carlotta Mutti¹⁵, Himasha Muvindi⁶⁵, Gugapriyaa Muyandy⁴³, Dimitra Melia Myrodia⁷⁹,
Farah Nadia Mohd-Hanafiah⁴¹⁶, Dave Nagpal⁴²⁰, Alex Nagrebetsky⁴³⁰,
Mangala Narasimhan²⁷, Nageswaran Narayanan²¹, Alasdair Nazerali-Maitland⁴³¹,
Nadège Neant¹⁰, Holger Neb¹⁴, Coca Necsoi⁴³², Nikita Nekliudov¹⁴⁵, Erni Nelwan⁴³³,
Raul Neto¹⁶, Emily Neumann²⁴⁷, Pauline Yeung Ng⁴³⁴, Anthony Nghi²⁶³, Duc Nguyen⁴³⁵,
Orna Ni Choileain²⁶, Niamh Ni Leathlobhair²⁹, Alistair D Nichol⁴³⁶,
Prompak Nitayavardhana⁴³⁷, Stephanie Nonas⁴³⁸, Nurul Amani Mohd Noordin³⁵⁵,
Marion Noret¹⁰, Nurul Faten Izzati Norharizam⁴³, Lisa Norman²³⁷, Anita North⁴³⁹,
Alessandra Notari²⁷⁴, Mahdad Noursadeghi⁴⁴⁰, Karolina Nowicka⁴⁴ⁱ, Adam Nowinski³⁹⁵,
Saad Nseir³⁷, Jose I Nunez²⁸², Nurnaningsih Nurnaningsih⁴⁴², Dwi Utomo Nusantara⁴⁴³,
Elsa Nyamankolly¹¹⁹, Fionnuala O Brien¹³⁹, Annmarie O Callaghan¹³⁹,
Annmarie O'Callaghan⁴⁴⁴, Giovanna Occhipinti⁵⁸, Derbrenn O'Connor²⁷², Max O'Donnell²⁰⁵,
Tawnya Ogston⁴⁴⁵, Takayuki Ogura⁴⁰⁰, Tak-Hyuk Oh⁴⁴⁶, Sophie O'Halloran¹⁸⁸,
Katie O'Hearn²⁸⁸, Shinichiro Ohshimo⁴⁴⁷, Agnieszka Oldakowska⁴⁴¹, João Oliveira⁴¹,
Larissa Oliveira⁴⁴⁸, Piero L. Olliaro⁴, Conar O'Neil⁴⁴⁹, David S. Y. Ong⁴⁵⁰, Jee Yan Ong⁹⁹,
Wilna Oosthuyzen³⁰⁷, Anne Opavsky⁴⁵¹, Peter Openshaw⁴⁵²,
Claudia Milena Orozco-Chamorro¹²⁰, Jamel Ortoleva⁴⁵³, Javier Osatnik³⁴⁷, Linda O'Shea³⁰⁰,
Miriam O'Sullivan³⁶, Siti Zubaidah Othman⁵⁰, Nadia Ouamara¹⁴⁸, Rachida Ouissa²⁰⁶,
Eric Oziol¹⁶², Maïder Pagadoy¹²⁸, Justine Pages¹⁰, Amanda Palacios⁴⁵⁴, Massimo Palmarini²⁰⁷,
Giovanna Panarello⁵⁸, Prasan Kumar Panda⁴⁵⁵, Lai Hui Pang⁴³, Mauro Panigada¹⁹²,
Nathalie Pansu³⁸⁶, Aurélie Papadopoulos¹⁰, Paolo Parducci¹⁵,
Edwin Fernando Paredes Oña⁴⁵⁶, Rachael Parke⁴⁵⁷, Melissa Parker³⁶⁴, Jérémie Pasquier¹⁴⁷,
Bruno Pastene¹⁰⁶, Fabian Patauner⁴⁶, Drashti Patel³²⁹, Mohan Dass Pathmanathan¹⁷⁷,
Luís Patrão²¹³, Patricia Patricio⁴⁵⁸, Juliette Patrier¹²², Laura Patrizi¹⁵, Lisa Patterson⁴⁵⁹,
Christelle Paul¹⁰, Mical Paul²³⁵, Jorge Paulos⁴², William A. Paxton⁴²⁵, Jean-François Payen¹²⁹,
Sandra L Peake¹⁴¹, Kalaiarasu Peariasamy², Giles J. Peek⁴⁰¹, Florent Peelman⁴⁶⁰,
Nathan Peiffer-Smadja¹⁰, Vincent Peigne¹⁵⁸, Mare Pejkovska⁴¹⁹, Paolo Pelosi⁸⁷,
Ithan D. Peltan³³⁷, Rui Pereira⁴², Daniel Perez¹²⁰, Luis Periel¹⁰⁷, Thomas Perpoint⁹⁸,
Antonio Pesenti¹⁹², Vincent Pestre²⁹⁰, Lenka Petrou⁴⁶¹, Michele Petrovic⁴⁶²,
Ventzislava Petrov-Sanchez¹⁰, Frank Olav Pettersen²⁴⁵, Gilles Peytavin¹⁰, Scott Pharand⁴⁵,
Ooyanong Phonemixay¹⁸⁰, Soulichanya Phoutthavong¹⁶⁹, Michael Piagnerelli¹¹¹,
Walter Picard²⁷⁸, Olivier Picone¹⁰, Maria de Piero⁴⁶³, Carola Pierobon¹⁵⁸, Djura Piersma⁹³,
Carlos Pimentel⁴², Raquel Pinto²⁰³, Valentine Piquard¹²², Catarina Pires⁴²,
Isabelle Pironneau³⁸⁷, Lionel Piroth¹¹⁶, Roberta Pisi¹⁵, Ayodhia Pitaloka²⁰, Chiara Piubelli¹⁰⁹,
Riinu Pius²³⁷, Laurent Plantier²⁶⁴, Hon Shen Png⁵⁰, Julien Poissy³⁷, Ryadh Pokeerbux²⁷⁵,
Maria Pokorska-Spiewak⁴⁴¹, Sergio Poli²⁰⁴, Georgios Pollakis⁴²⁵, Diane Ponscarme²²⁰,
Jolanta Popielska⁴⁴¹, Diego Bastos Porto⁹⁷, Andra-Maris Post⁴⁶⁴, Douwe F. Postma²³³,
Pedro Povoa¹⁶³, Diana Póvoas²⁰³, Jeff Powis²⁰², Sofia Prapa⁷⁹, Viladeth Praphasiri¹³¹,
Sébastien Preau³⁷, Christian Prebensen¹⁰¹, Jean-Charles Preiser¹¹, Anton Prinssen²⁵⁶,
Mark G. Pritchard⁴, Gamage Dona Dilanthi Priyadarshani⁶⁵, Lucia Proença⁴¹,
Sravya Pudota¹⁶⁰, Oriane Puéchal¹⁰, Bambang Pujo Semedi⁴⁶⁵, Matteo Puntoni¹⁵,
Gregory Purcell²⁴¹, Luisa Quesada²⁰⁴, Vilmaris Quinones-Cardona²¹⁷, Else Quist-Paulsen²⁴⁵,
Mohammed Quraishi¹⁵², Maia Rabaa¹²¹, Christian Rabaud¹⁰⁵, Aldo Rafael⁴⁶⁶, Marie Rafiq²¹⁹,
Gabrielle Ragazzo¹⁴⁹, Mutia Rahardjani²⁰, Ahmad Kashfi Haji Ab Rahman³⁹³,
Rozanah Abd Rahman²⁹⁶, Fernando Rainieri⁴⁰³, Giri Shan Rajahram³,
Nagarajan Ramakrishnan⁴⁶⁷, José Ramalho⁴², Ahmad Afiq Ramli⁴³, Blandine Rammaert³⁸⁷,
Grazielle Viana Ramos⁴⁶⁸, Anais Rampello¹⁵, Rajavardhan Rangappa²⁷⁷, Ritika Ranjan²⁹,
Elena Ranza¹⁵, Christophe Rapp¹⁵⁶, Aasiyah Rashan⁴⁶⁹, Thalha Rashan⁶⁵, Menaldi Rasmin²⁵,
Indrek Rätsep⁴⁷⁰, Cornelius Rau³¹⁰, Francesco Rausa¹⁵, Tharmini Ravi⁴³, Andre Real⁴⁷¹,
Stanislas Rebaudet⁴⁷², Sarah Redl⁴⁷³, Brenda Reeve¹⁹⁶, Liadain Reid¹⁹,
Dag Henrik Reikvam²⁴⁵, Renato Reis²⁶², Jordi Rello⁴⁷⁴, Jonathan Remppis²⁹⁴, Martine Remy³⁷,
Hongru Ren⁴⁷⁵, Hanna Renk²⁹⁴, Anne-Sophie Resseguier²²¹, Matthieu Revest¹⁷³,
Oleksa Rewa³⁸⁵, Luis Felipe Reyes⁴⁷⁶, Tiago Reyes²⁸², Maria Ines Ribeiro¹⁵⁵,
Antonia Ricchiuto²¹⁵, David Richardson⁴⁰⁷, Denise Richardson¹⁰⁰, Laurent Richier⁷⁵,
Siti Nurul Atikah Ahmad Ridzuan³⁹³, Jordi Riera³⁰, Ana L Rios¹⁶, Asgar Rishu¹⁷⁰,
Patrick Rispal⁴⁷³, Karine Risso²²⁶, Maria Angelica Rivera Nuñez³⁴¹, Nicholas Rizer¹²,
Chiara Robba⁸⁷, André Roberto⁴¹, David L. Robertson²⁰⁷, Olivier Robineau²⁴⁹,
Ferran Roche-Campo²⁷³, Paola Rodari¹⁰², Simão Rodeia⁴¹, Julia Rodriguez Abreu⁴⁷⁷,
Bernhard Roessler¹⁴⁴, Claire Roger⁴⁷⁸, Pierre-Marie Roger²⁰⁶, Emmanuel Roilides³⁴³, Amanda Rojek⁴⁷⁹, Juliette Romaru⁸¹, Roberto Roncon-Albuquerque Jr²⁸⁰, Mélanie Roriz⁴⁷³, Manuel Rosa-Calatrava¹⁰, Michael Rose³¹⁸, Dorothea Rosenberger¹⁸⁴, Andrea Rossanese¹⁰², Matteo Rossetti⁵⁸, Sandra Rossi¹⁵, Bénédicte Rossignol¹⁰, Patrick Rossignol¹⁰, Stella Rousset²¹⁹, Carine Roy¹⁰, Benoît Roze²³, Desy Rusmawatiningtyas⁴⁴², Clark D. Russell¹¹⁸, Maeve Ryan²⁹, Maria Ryan¹⁶⁰, Steffi Ryckaert³²³, Aleksander Rygh Holten²⁴⁵, Isabela Saba²⁰⁴, Luca Sacchelli¹⁵, Musharaf Sadat⁷, Valla Sahraei²³⁹, Nadia Saidani⁴⁸⁰, Maximilien Saint-Gilles¹⁰⁵, Pranya Sakiyalak⁴³⁷, Leonardo Salazar⁴⁸¹, Gabriele Sales¹³⁸, Stéphane Sallaberry³⁴², Charlotte Salmon Gandonniere²⁶⁴, Hélène Salvator²⁷⁰, Olivier Sanchez⁶¹, Angel Sanchez-Miralles⁴⁸², Vanessa Sancho-Shimizu⁴⁸³, Gyan Sandhu³⁶³, Zulfiqar Sandhu³⁰⁰, Pierre-François Sandrine¹⁴⁷, Oana Sandulescu⁴⁸⁴, Marlene Santos³⁶³, Shirley Sarfo-Mensah⁴⁵, Bruno Sarmento Banheiro⁴⁸⁵, Iam Claire E. Sarmiento²⁷, Benjamine Sarton²¹⁹, Ankana Satya³²⁹, Sree Satyapriya¹⁰⁷, Rumaisah Satyawati⁴⁸⁶, Egle Saviciute¹⁹⁴, Parthena Savvidou³⁴³, Yen Tsen Saw³⁸⁸, Justin Schaffer⁴⁸⁷, Tjard Schermer⁴⁸⁸, Arnaud Scherpereel²⁷⁵, Marion Schneider¹⁰, Stephan Schroll⁴⁸⁹, Michael Schwameis¹⁴⁴, Gary Schwartz⁴⁹⁰, Brendan Scicluna²⁰⁰, Janet T. Scott²⁰⁷, James Scott-Brown³², Nicholas Sedillot¹²⁷, Tamara Seitz⁴⁹¹, Mageswari Selvarajoo⁴³, Caroline Semaille¹⁰, Malcolm G. Semple⁴⁹², Rasidah Bt Senian⁵⁰, Eric Senneville²⁴⁹, Claudia Sepulveda⁵⁷, Filipa Sequeira⁴⁹³, Tânia Sequeira⁴², Ary Serpa Neto¹⁴¹, Ellen Shadowitz¹⁷⁰, Syamin Asyraf Shahidan⁵⁰, Mohammad Shamsah²⁸, Anuraj Shankar²⁰, Shaikh Sharjeel³⁰⁰, Pratima Sharma²⁹⁷, Catherine A. Shaw²³⁷, Victoria Shaw⁴⁹⁴, Rajesh Mohan Shetty²⁷⁷, Rohan Shetty³²⁹, Haixia Shi¹⁰⁷, Nisreen Shiban¹⁴⁹, Mohiuddin Shiekh⁶⁵, Takuya Shiga⁴⁹⁵, Nobuaki Shime³⁴⁵, Hiroaki Shimizu⁴⁹⁶, Keiki Shimizu⁴⁹⁷, Sally Shrapnel¹⁹⁷, Hoi Ping Shum⁴⁹⁸, Nassima Si Mohammed¹⁰, Ng Yong Siang²⁹⁶, Jeanne Sibiude⁴⁰⁹, Bountoy Sibounheuang¹⁸⁰, Atif Siddiqui⁴⁹⁹, Louise Sigfrid⁴, Piret Sillaots⁴⁶⁴, Catarina Silva⁴⁰, Maria Joao Silva⁵⁶, Rogério Silva⁸⁰, Benedict Sim Lim Heng⁵⁰⁰, Wai Ching Sin⁴³⁴, Dario Sinatti²¹⁵, Budha Charan Singh⁴⁵⁵, Punam Singh²⁰⁹, Pompini Agustina Sitompul⁴⁸⁶, Karisha Sivam⁴³, Vegard Skogen³⁶⁷, Sue Smith²²⁷, Benjamin Smood¹², Coilin Smyth²⁰⁹, Michelle Smyth¹⁹, Morgane Snacken¹¹, Dominic So³⁵⁶, Tze Vee Soh⁴²⁷, Joshua Solomon⁷⁰, Tom Solomon³²¹, Emily Somers²⁹⁷, Agnès Sommet³¹², Myung Jin Song⁵⁰¹, Rima Song³¹⁴, Tae Song¹⁹³, Jack Song Chia²⁹, Michael Sonntagbauer¹⁴, Azlan Mat Soom³³³, Albert Sotto³⁸², Edouard Soum⁴⁶⁰, Ana Chora Sousa¹⁵⁵, Marta Sousa²⁶², Maria Sousa Uva²⁶², Vicente Souza-Dantas⁵⁰², Alexandra Sperry¹², Elisabetta Spinuzza³¹, B. P. Sanka Ruwan Sri Darshana⁶⁵, Shiranee Srikandan¹⁹⁵, Sarah Stabler³⁷, Thomas Staudinger¹⁴⁴, Stephanie-Susanne Stecher⁶⁰, Trude Steinsvik⁵⁰³, Ymkje Stienstra²³³, Birgitte Stiksrud²⁴⁵, Eva Stolz²⁶, Amy Stone¹³⁹, Adrian Streinu-Cercel⁵⁰⁴, Anca Streinu-Cercel⁵⁰⁵, Ami Stuart¹⁸⁴, David Stuart⁵⁰⁶, Richa Su¹²², Decy Subekti²⁰, Gabriel Suen⁵⁰⁷, Jacky Y. Suen¹⁹⁷, Asfia Sultana²⁴², Charlotte Summers⁵⁰⁸, Dubravka Supic¹⁸⁸, Deepashankari Suppiah⁹⁹, Magdalena Surovcová⁴⁶¹, Atie Suwarti²⁰, Andrey Svistunov¹⁴⁵, Sarah Syahrin⁴³, Konstantinos Syrigos⁷⁹, Jaques Sztajnbok⁸², Konstanty Szuldrzynski⁵⁰⁹, Shirin Tabrizi⁷⁰, Fabio S. Taccone²⁹⁵, Lysa Tagherset¹⁰, Shahdattul Mawarni Taib⁵⁰, Ewa Talarek⁴⁴¹, Sara Taleb²², Jelmer Talsma⁴⁸⁸, Renaud Tamisier⁵¹⁰, Maria Lawrensia Tampubolon⁴⁸⁶, Kim Keat Tan⁴³, Le Van Tan³⁶², Yan Chyi Tan⁴³, Clarice Tanaka⁵¹¹, Hiroyuki Tanaka⁵¹², Taku Tanaka³²⁸, Hayato Taniguchi⁵¹³, Coralie Tardivon¹⁰, Pierre Tattevin¹⁷³, M Azhari Taufik⁵¹⁴, Hassan Tawfik²⁹, Richard S. Tedder⁵¹⁵, Tze Yuan Tee³⁵⁴, João Teixeira⁴², Sofia Tejada⁸⁵, Marie-Capucine Tellier¹⁰, Sze Kye Teoh³⁵⁰, Vanessa Teotonio⁵¹⁶, François Téoulé¹⁰, Pleun Terpstra⁴⁸⁸, Olivier Terrier¹⁰, Nicolas Terzi¹²⁹, Hubert Tessier-Grenier¹²⁶, Adrian Tey¹⁶⁰, Alif Adlan Mohd Thabit⁴³, Zhang Duan Tham⁵⁰, Suvintheran Thangavelu⁴³, Vincent Thibault¹⁷³, Simon-Djamel Thiberville⁵¹⁷, Benoît Thill¹⁶², Jananee Thirumanickam⁴³, Shaun Thompson⁵¹⁸, David Thomson⁵¹⁹, Emma C. Thomson²⁰⁷, Surain Raaj Thanga Thurai⁴³, Duong Bich Thuy³⁶², Ryan S. Thwaites⁴⁵², Andrea Ticinesi¹⁵, Paul Tierney²³⁸, Vadim Tieroshyn²⁸⁴, Peter S Timashev¹⁴⁵, Jean-François Timsit¹⁰, Noémie Tissot¹²⁸, Jordan Zhien Yang Toh⁵⁰, Maria Toki⁷⁹, Kristian Tonby²⁴⁵, Sia Loong Tonnii⁹⁹, Marta Torre⁴², Antoni Torres⁴²⁶, Margarida Torres²⁰³, Rosario Maria Torres Santos-Olmo³⁴¹, Hernando Torres-Zevallos⁵²⁰, Michael Towers²⁶, Tony Trapani⁵²¹, Huynh Trung Trieu³⁶², Théo Trioux¹⁰, Cécile Tromeur⁴⁹, Ioannis Trontzas⁷⁹,
Tiffany Trouillon¹²⁴, Jeanne Truong⁵²², Christelle Tual¹⁰, Sarah Tubiana¹⁰, Helen Tuite²⁹, Alexis F. Turgeon⁹⁵, Jean-Marie Turmel¹⁴⁷, Lance C. W. Turtle³²¹, Anders Tveita⁵⁰³, Pawel Twardowski⁵²³, Makoto Uchiyama⁴⁴⁵, PG Ishara Udayanga⁶⁵, Andrew Udy¹⁴¹, Roman Ullrich¹⁴⁴, Alberto Uribe¹⁰⁷, Asad Usman¹², Timothy M. Uyeki⁹², Cristinava Vajdovics¹⁸, Piero Valentini²¹⁵, Luís Val-Flores⁴², Amélie Valran³⁴², Ilaria Valzano¹⁵, Stijn Van de Velde³²³, Marcel van den Berge⁵²⁴, Machteld Van der Feltz²²⁴, Job van der Palen⁹³, Paul van der Valk⁹³, Nicky Van Der Vekens³²³, Peter Van der Voort²³³, Sylvie Van Der Werf¹⁰, Laura van Gulik⁵²⁵, Jarne Van Hattem²⁰¹, Carolien van Netten⁵²⁶, Frank van Someren Greve⁵, Gitte Van Twillert⁵²⁷, Ilonka van Veen⁹³, Hugo Van Willigen²⁰⁰, Noémie Vanel¹⁰, Henk Vanoverschelde³²³, Pooja Varghese¹⁹, Michael Varrone²⁷, Shoban Raj Vasudayan³³³, Charline Vauchy¹²⁸, Shaminee Veeran⁴³, Aurélie Veislinger¹⁰, Sebastian Vencken¹⁹, Sara Ventura⁴¹, Annelies Verbon³⁰⁹, James Vickers³⁴⁸, José Ernesto Vidal⁸², César Vieira⁴², Joy Ann Villanueva²², Judit Villar¹⁵³, Pierre-Marc Villeneuve⁵²⁸, Andrea Villoldo²⁵⁴, Nguyen Van Vinh Chau³⁶², Gayatri Vishwanathan³²⁹, Benoit Visseaux¹⁰, Hannah Visser⁵²⁹, Chiara Vitiello⁵⁸, Manivanh Vongsouvath¹⁸⁰, Harald Vonkeman⁹³, Fanny Vuotto³⁷, Noor Hidayu Wahab⁵⁰, Suhaila Abdul Wahab⁸, Nadirah Abdul Wahid⁴³, Marina Wainstein⁸³, Steve Webb⁹², Jia Wei³¹⁷, Katharina Weil¹²³, Tan Pei Wen³³³, Sanne Wesselius¹³⁴, T. Eoin West⁸⁹, Murray Wham⁴²³, Bryan Whelan³⁶, Paul Henri Wicky¹²², Aurélie Wiedemann¹⁰, Surya Otto Wijaya⁴⁸⁶, Keith Wille³³⁴, Sue Willems⁵³⁰, Patricia J Williams¹⁴¹, Virginie Williams¹⁶⁵, Evert-Jan Wils⁵³¹, Ng Wing Yiu⁵³², Calvin Wong²²⁸, Teck Fung Wong⁹⁹, Xin Ci Wong², Yew Sing Wong⁴³, Natalie Wright³⁵, Gan Ee Xian⁵⁰, Lim Saio Xian¹⁸¹, Kuan Pei Xuan⁴³, Ioannis Xynogalas⁷⁹, Sophie Yacoub³⁶², Siti Rohani Binti Mohd Yakop⁵⁰, Masaki Yamazaki⁵³³, Elizabeth Yarad¹⁴¹, Yazdan Yazdanpanah¹⁰, Nicholas Yee Liang Hing⁵⁰, Cécile Yelnik²⁷⁵, Chian Hui Yeoh³³², Stephanie Yerkovich¹⁹⁷, Touxiong Yiaye¹³¹, Toshiki Yokoyama⁵³⁴, Hodane Yonis¹¹², Obada Yousif³⁰⁰, Saptadi Yuliarto³²⁷, Akram Zaaqoq⁵³⁵, Marion Zabbe⁴⁶⁰, Gustavo E Zabert⁵³⁶, Kai Zacharowski¹⁴, Masliza Zahid⁵³⁷, Maram Zahran²³⁵, Nor Zaila Binti Zaidan³⁵⁵, Maria Zambon⁵³⁸, Miguel Zambrano⁵⁷, Alberto Zanella¹⁹², Konrad Zawadka⁴⁴¹, Nurul Zaynah⁵⁰, Hiba Zayyad²⁹¹, Alexander Zoufaly⁴⁹¹, David Zucman²⁷⁰ & Mazankowski Heart Institute⁵³⁹

⁷King Abdulaziz Medical City, Riyadh, Saudi Arabia. ⁸Tuanku Fauziah Hospital, Perlis, Malaysia. ⁹Chiba University Hospital, Chiba, Japan. ¹⁰INSERM, Paris, France. ¹¹CUB-Hopital Erasme, Anderlecht, Belgium. ¹²Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA. ¹³Rinku General Medical Center, Osaka, Japan. ¹⁴Uniklinik University Hospital, Frankfurt, Germany. ¹⁵University Hospital of Parma, Parma, Italy. ¹⁶Centro Hospitalar Vila Nova de Gaia/Espinho, Espinho, Portugal. ¹⁷King Faisal Hospital Research Center, Riyadh, Saudi Arabia. ¹⁸University Hospital, Kerry, Ireland. ¹⁹St Vincents University Hospital, Dublin, Ireland. ²⁰Murni Teguh Memorial Hospital, Bunda Thamrin Hospital, North Sumatera, Indonesia. ²¹Our lady of Lourdes Drogheda, Drogheda, Ireland. ²²Hamad General Hospital, Doha, Qatar. ²³Centre Hospitalier de Saintonge, Saintes, France. ²⁴Teine Keijinkai Hospital, Sapporo, Japan. ²⁵Persahabatan Hospital, Jakarta, Indonesia. ²⁶Beaumont Hospital, Dublin, Ireland. ²⁷Northwell Health, New York, USA. ²⁸Al-Adan Hospital, Hadiya, Kuwait. ²⁹Galway University Hospital, Galway, Ireland. ³⁰Hospital Vall d'Hebron, Barcelona, Spain. ³¹University Hospital Policlinico Paolo Giaccone, Palermo, Italy. ³²School of Informatics, University of Edinburgh, Edinburgh, UK. ³³Centre Hospitalier Universitaire Rouen (Center Hospitalier Universitaire de Rouen), Rouen, France. ³⁴Al-Amiri & Jaber Al-Ahmed Hospitals, Kuwait City, Kuwait. ³⁵St Bernard's Hospital, Gibraltar, Gibraltar. ³⁶Sligo University Hospital (Saolta), Sligo, Ireland. ³⁷Centre Hospitalier Universitaire de Lille, Lille, France. ³⁸Centre Hospitalier Universitaire de Nantes (Hôpital femme-enfant-adolescent), Nantes, France. ³⁹Centre Hospitalier Félix-Guyon, Saint-Denis, Réunion. ⁴⁰Centro Hospital e Universitário de Coimbra, Coimbra, Portugal. ⁴¹Hospital de São José -U.U.M, Lisbon, Portugal. ⁴²Hospital Curry Cabral - Intensive Care Unit - UCIP7, Lisbon, Portugal. ⁴³Sungai Buloh Hospital, Selangor, Malaysia. ⁴⁴Catharina Ziekenhuis, Eindhoven, The Netherlands. ⁴⁵The Ottawa Hospital, Ottawa, Canada. ⁴⁶University of Campania, Carseta, Italy. ⁴⁷Centre Hospitalier Universitaire Amiens-Picardie, Amiens, France. ⁴⁸Department of Infectious - Tropical Diseases and Microbiology, IRCCS Sacro Cuore Hospital - Negrar, Negrar di Valpolicella, Italy. ⁴⁹Centre Hospitalier Universitaire de Brest, Brest, France. ⁵⁰Kuala Lumpur Hospital, WPKL, Kuala Lumpur, Malaysia. ⁵¹Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy. ⁵²Centro de Pesquisa Aggeu Magalhães, Fiocruz, Recife, Brazil. ⁵³NICVD Dhaka, Dhaka, Bangladesh. ⁵⁴National Cardiovascular Center Harapan Kita Jakarta Indonesia, Jakarta, Indonesia. ⁵⁵Intensive Care Department, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia. ⁵⁶Centro Hospitalar Universitário do Porto (CHUP), Porto, Portugal. ⁵⁷Instituto Nacional Del Tórax, Santiago, Chile. ⁵⁸Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione, Palermo, Italy. ⁵⁹CISS Chaudière-Appalaches, Québec, Canada. ⁶⁰Medical Department II, LMU Hospital Munich, Campus Großhadern, Munich, Germany. ⁶¹Hôpital Européen Georges-Pompidou AP-HP, Paris, France. ⁶²Groote Schuur Hospital, Cape Town, South Africa. ⁶³University of Iowa, Iowa City, USA. ⁶⁴St. Boniface Hospital, Winnipeg, Manitoba, Canada. ⁶⁵CCA Network, Asia, Chiang Mai, Thailand. ⁶⁶Fujieda Municipal General Hospital, Fujieda, Japan. ⁶⁷Hospital Puerta de Hierro Majadahonda, Madrid, Spain. ⁶⁸Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Vientiane, Laos, Thailand. ⁶⁹Centre Hospitalier intercommunal de Créteil, Créteil, France. ⁷⁰McGill
University Health Centre, Montreal, Canada. ⁷¹Centre Hospitalier de Cholet, Cholet, France. ⁷²Centre Hospitalier de Perpignan, Perpignan, France. ⁷³Hôpital Lariboisière AP-HP, Paris, France. ⁷⁴Hôpital Cochin AP-HP, Paris, France. ⁷⁵Centre Hospitalier Intercommunal Villeneuve-Saint-Georges, Villeneuve-Saint-Georges, France. ⁷⁶Grande Prairie Queen Elizabeth II, Grande Prairie, Canada. ⁷⁷Roslin Institute, University of Edinburgh, Edinburgh, UK. ⁷⁸Rush University Medical Center, Chicago, USA. ⁷⁹Sotiria General Hospital, Athens, Greece. ⁸⁰Unidade Local de Saúde de Alto Minho, Viana Do Castelo, Portugal. ⁸¹Centre Hospitalier Universitaire de Reims, Reims, France. ⁸²Instituto de Infectologia Emílio Ribas, Sao Paulo, Brazil. ⁸³Caja Nacional De Salud, Trinidad, Bolivia. ⁸⁴Section of Molecular Virology, Imperial College London, London, UK. ⁸⁵Hospital Universitario de Alava, Araba, Spain. ⁸⁶Grand Hôpital de l'Est Francilien (Site de Marne-la-Vallée), Jossigny, France. ⁸⁷San Martino Hospital, Genoa, Italy. ⁸⁸Clinica Valle de Lilli, Valle del Cauca, Colombia. ⁸⁹University of Washington Medical Center - Northwest, Seattle, USA. ⁹⁰Critical Care Asia, Bangkok, Thailand. ⁹¹Raja Permaisuri Bainun Hospital, Perak, Malaysia. ⁹²WHO-ISARIC Clinical Characterisation Protocol & SPRINT-SARI, London, UK. ⁹³Medisch Spectrum Twente, Zutphen, The Netherlands. ⁹⁴Tartu University Hospital, Tartu, Estonia. ⁹⁵Hôpital de l'Enfant-Jésus, Quebec, Quebec, Canada. ⁹⁶Follow Up Study Working Group, London, UK. ⁹⁷Sao Camilo Cura D'ars, Fortaleza, Brazil. ⁹⁸Centre Hospitalier Universitaire de Lyon - HCL, Lyon, France. ⁹⁹Sarawak General Hospital, Sarawak, Malaysia. ¹⁰⁰University of Cincinnati, Cincinnati, USA. ¹⁰¹Akershus University Hospital, Nordbyhagen, Norway. ¹⁰²Ospedale Sacro Cuore Don Calabria, Negrar Di Valpolicella, Italy. ¹⁰³Hôpital Raymond-Poincaré, Garches, France. ¹⁰⁴Oklahoma Heart Institute, Oklahoma, USA. ¹⁰⁵Centre Hospitalier Régional et Universitaire de Nancy - Hôpitaux de Brabois, France. ¹⁰⁶Hôpital de la Timone, Marseille, France. ¹⁰⁷Ohio State University, Columbus, USA. ¹⁰⁸All India Institute of Medical Sciences, Rishikesh, India. ¹⁰⁹Department of Infectious, Tropical Diseases and Microbiology, IRCCS Sacro Cuore Don Calabria Hospital, Negrar, Italy. ¹¹⁰Thonon-les-Bains, Thonon-les-Bains, France. ¹¹¹Civil Hospital Marie Curie, Charleroi, Belgium. ¹¹²Hôpital Lyon Sud - HCL, Lyon, France. ¹¹³The Centre hospitalier universitaire Sainte-Justine, Montreal, Canada. ¹¹⁴Rio Hortega University Hospital, Valladolid, Spain. ¹¹⁵Jena University Hospital, Jena, Germany. ¹¹⁶Centre Hospitalier Universitaire Mitterrand Dijon-Bourgogne, Dijon, France. ¹¹⁷National Institute for Communicable Diseases, Johannesburg, South Africa. ¹¹⁸Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh, UK. ¹¹⁹Centre Hospitalier de Dax - Côte d'Argent, Dax, France. ¹²⁰Universidad del Cauca, Cauca, Colombia. ¹²¹Pratama Rada Bolo Hospital, Karitas Hospital and Waikabubak Hospital, Sumba, Indonesia. ¹²²Hôpital Bichat Claude-Bernard AP-HP, Paris, France. ¹²³University Hospital Dusseldorf, Dusseldorf, Germany. ¹²⁴Centre Hospitalier Universitaire de Saint-Étienne, Saint-Étienne, France. ¹²⁵Hôpital Avicenne, Bobigny, France. ¹²⁶Centre hospitalier de l'université de Montréal, Montreal, Canada. ¹²⁷Centre Hospitalier de Bourg-en-Bresse, Bourg-en-Bresse, France. ¹²⁸Centre Hospitalier Universitaire de Besançon, Besançon, France. ¹²⁹Centre Hospitalier Universitaire Grenoble-Alpes, Grenoble, France. ¹³⁰Luang Namtha Provincial Hospital, Luang Namtha, Laos. ¹³¹Xieng Khouang Provincial Hospital, Phonsavan, Laos. ¹³²Centre Hospitalier Universitaire de Nantes (Hôtel-Dieu), Nantes, France. ¹³³Hôpital d'Instruction des Armées Bégin, Saint-Mandé, France. ¹³⁴Franciscus Gasthuis, Rotterdam, The Netherlands. ¹³⁵Ministry of Health, and D'Or Institute of Research and Education (IDOR), National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation (INI-FIOCRUZ), Rio de Janeiro, Brazil. ¹³⁶Centre Hospitalier de Mayotte, Mamoudzou, Mayotte. ¹³⁷Hospital Egas Moniz, Lisboa, Portugal. ¹³⁸Ospedale Molinette, Torino, Italy. ¹³⁹Cork University Hospital, Cork, Ireland. ¹⁴⁰Beacon Hospital, Dublin, Ireland. ¹⁴¹Monash University, Melbourne, Australia. ¹⁴²Nelson Hospital, Nelson, New Zealand. ¹⁴³Cleveland Clinic, Weston, USA. ¹⁴⁴Medical University of Vienna, Vienna, Austria. ¹⁴⁵Sechenov University, Moscow, Russia. ¹⁴⁶Clinica Universidade de La Sabana, Cundinamarca, Chia, Colombia. ¹⁴⁷Centre Hospitalier Universitaire de Martinique, Fort-de-France, Saint Martin, France. ¹⁴⁸Centre Hospitalier Régional Metz-Thionville, Metz, France. ¹⁴⁹Emory University Healthcare System, Atlanta, USA. ¹⁵⁰Johns Hopkins, Baltimore, USA. ¹⁵¹Comissão de Ética, Unidade Local de Saúde de Matosinhos, Porto, Portugal. ¹⁵²Presbyterian Hospital Services, Alberquerque, USA. ¹⁵³Hospital del Mar, Barcelona, Spain. ¹⁵⁴Reina Sofía University Hospital, Cordoba, Spain. ¹⁵⁵Hospital Espírito Santo de Évora, Évora, Portugal. ¹⁵⁶Hôpital Américain de Paris, Neuilly-sur-Seine, France. ¹⁵⁷Vancouver Island Health, Vancouver, Canada. ¹⁵⁸Centre Hospitalier Métropole Savoie, Chambéry, France. ¹⁵⁹Department of Anesthesiology, Centre hospitalier de l'Université de Montréal, Montreal, USA. ¹⁶⁰University Hospital - Limerick, Limerick, Ireland. ¹⁶¹Centro Hospitalar e Universitário de Coimbra - Hospital Pediátrico, Coimbra, Portugal. ¹⁶²Centre Hospitalier de Béziers, Béziers, France. ¹⁶³Hospital São Francisco Xavier, Lisbon, Portugal. ¹⁶⁴Policlinicodi Orsola Universitàdi Bologna, Bologna, Italy. ¹⁶⁵Hospital du Sacre Coeur, Montreal, Canada. ¹⁶⁶Hospital Universitari Sagrat Cor, Barcelona, Spain. ¹⁶⁷Avera McKennan Hospital, Sioux Falls, USA. ¹⁶⁸Centre Hospitalier de Melun, Melun, France. ¹⁶⁹Attapeu Provincial Hospital, Attapeu, Laos. ¹⁷⁰Sunnybrook Health Sciences Centre, Toronto, Canada. ¹⁷¹Antimicrobial Resistance and Hospital Acquired Infection Department, Public Health England, London, UK. ¹⁷²Hôpital Kremlin-Bicêtre, Le Kremlin-Bicêtre, France. ¹⁷³Centre Hospitalier Universitaire Rennes (Hôpital Pontchaillou), Rennes, France. ¹⁷⁴Hôpital Tenon AP-HP, Paris, France. ¹⁷⁵University of Guadalajara Health Sciences Center, Guadalajara, Mexico. ¹⁷⁶Hôpital Saint-Antoine AP-HP, Paris, France. ¹⁷⁷National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia. ¹⁷⁸Ospedale San Paolo, Milan, Italy. ¹⁷⁹Chonnam National University Hospital, Dong-gu, South Korea. ¹⁸⁰Salavan Provincial Hospital, Salavan, Laos. ¹⁸¹Pulau Pinang Hospital, Pulau Pinang, Malaysia. ¹⁸²Sunway Medical Centre, Selangor, Malaysia. ¹⁸³University Hospital Virgen del Rocío/Institute of Biomedicine of Seville, Seville, Spain. ¹⁸⁴University of Utah, Salt Lake City, USA. ¹⁸⁵Children's Health Ireland, Dublin, Ireland. ¹⁸⁶Roslin Institute, University of Edinburgh, Easter Bush, Edinburgh, UK. ¹⁸⁷Foothills Medical Centre, Calgary, Canada. ¹⁸⁸Connolly Hospital Blanchardstown, Dublin, Ireland. ¹⁸⁹Carilion Clinic, Roanoke, USA. ¹⁹⁰Centre Hospitalier Départemental Vendée, La Roche-sur-Yon, France. ¹⁹¹Allegheny General Hospital, Pittsburgh, USA. ¹⁹²Fondazione IRCCS Ca, Milan, Italy. ¹⁹³University of Chicago, Chicago, USA. ¹⁹⁴Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK. ¹⁹⁵Department of Infectious Disease, Imperial College London, London, UK. ¹⁹⁶Brantford General Hospital, Brantford, Canada. ¹⁹⁷University of Queensland, Brisbane, Australia. ¹⁹⁸Centre Hospitalier Bretagne Atlantique, Vannes, France. ¹⁹⁹Hôpital Jacques Monod, Le Havre, France. ²⁰⁰PREPARE and RECOVER EU Consortium, Brussels, Belgium. ²⁰¹Tergooi Hospital,
Hilversum, The Netherlands. ²⁰²Michael Garron Hospital, Toronto, Canada. ²⁰³Hospital de Curry Cabral - Infectious Diseases, Lisbon, Portugal. ²⁰⁴Mount Sinai Medical Center, Miami, FL, USA. ²⁰⁵Columbia University, New York, USA. ²⁰⁶Centre Hospitalier Universitaire de Guadeloupe, Pointe-à-Pitre, Guadeloupe. ²⁰⁷MRC-University of Glasgow Centre for Virus Research, 464 Bearsden Road, Glasgow, UK. ²⁰⁸UH Cleveland Hospital, Cleveland, USA. ²⁰⁹University Hospital - Waterford, Waterford, Ireland. ²¹⁰Saint-Martin, Saint-Martin, Guadeloupe. ²¹¹Department of Statistics, University of Oxford, Oxford, UK. ²¹²Centre hospitalier Universitaire de Sherbrooke, Sherbrooke, Canada. ²¹³Centro Hospitalar de Tondela-Viseu, Viseu, Portugal. ²¹⁴Monaldi Hospital, Napoli, Italy. ²¹⁵Università Cattolica del Sacro Cuore, Rome, Italy. ²¹⁶Department of Infection, Immunity and Cardiovascular Disease, The Florey Institute for Host-Pathogen Interactions, University of Sheffield, Sheffield, UK. ²¹⁷St Christopher's Hospital for Children, Philadelphia, USA. ²¹⁸Piedmont Atlanta Hospital, Atlanta, Georgia, USA. ²¹⁹Hôpital Purpan, Toulouse, France. ²²⁰Hôpital Saint-Louis AP-HP, Paris, France. ²²¹Centre Hospitalier Emile Roux, Le Puy-en-Velay, France. ²²²Hôpital Bel-Air, Thionville, France. ²²³Centre Hospitalier Universitaire Toulouse (IUCT), Toulouse, France. ²²⁴Alrijne Hospital, Leiden, The Netherlands. ²²⁵Policlinico of Padova, Padova, Italy. ²²⁶Centre Hospitalier Universitaire de Nice (Hôpital Archet), Nice, France. ²²⁷ISARIC Global Support Centre, Oxford, UK. ²²⁸Universitair Ziekenhuis, Gent, Belgium. ²²⁹INOVA Fairfax Medical Center, Fairfax, Virginia, USA. ²³⁰Hospital Universitario Dr Negrín, Las Palmas, Spain. ²³¹Hospital Professor Doutor Fernando Fonseca, Amadora, Portugal. ²³²Clinica Las Condes, Santiago, Chile. ²³³University Medical Center Groningen, Groningen, The Netherlands. ²³⁴Centre Hospitalier Mont-de-Marsan, Mont-de-Marsan, France. ²³⁵Rambam Hospital, Haifa, Israel. ²³⁶Centre Hospitalier Andrée Rosemon, Cayenne, French Guiana. ²³⁷Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK. ²³⁸Tallaght University Hospital, Dublin, Ireland. ²³⁹Lions Gate Hospital, Vancouver, Canada. ²⁴⁰Flevoziekenhuis, Almere, The Netherlands. ²⁴¹St James's Hospital, Dublin, Ireland. ²⁴²St Joseph's Health Center, Sherbrooke, Canada. ²⁴³Centre Hospitalier Universitaire d'Angers, Angers, France. ²⁴⁴Houston Methodist Hospital, Houston, TX, USA. ²⁴⁵Oslo University Hospital, Oslo, Norway. ²⁴⁶Cleveland Clinic, Ohio, OH, USA. ²⁴⁷Medical College of Wisconsin, Wisconsin, USA. ²⁴⁸Hôpital de la Conception, Marseille, France. ²⁴⁹Centre Hospitalier de Tourcoing, Tourcoing, France. ²⁵⁰Centre Hospitalier De Chateaudun, Route De Jallans, 28200 Chateaudun, France. ²⁵¹Reinier de Graaf Gasthuis, Delft, The Netherlands. ²⁵²Centre Hospitalier Universitaire Rennes (Hôpital Sud), Rennes, France. ²⁵³Tohoku Medical and Pharmaceutical University, Sendai, Japan. ²⁵⁴Plata Medical Foundation Private Community Hospital, Mar Del, Plata, Argentina. ²⁵⁵Hospitales Puerta de Hierro, Jalisco, Mexico. ²⁵⁶Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands. ²⁵⁷Centre Hospitalier Pierre Oudot, Bourgoin-Jallieu, France. ²⁵⁸North York General Hospital, Toronto, Canada. ²⁵⁹Hospital Universiti Sains Malaysia (Mix medical surgical ICU), Kota Bharu, Malaysia. ²⁶⁰Adult ICU Saiful Anwar Hospital, Malang, Indonesia. ²⁶¹University of California San Francisco - Fresno, Fresno, USA. ²⁶²Hospital Santa Maria, Centro Hospitalar Universitário Lisboa Norte, Amadora, Portugal. ²⁶³Centre Hospitalier Techer, Calais, France. ²⁶⁴Centre Hospitalier Régional et Universitaire de Tours, Tours, France. ²⁶⁵Liverpool School of Tropical Medicine, Liverpool, UK. ²⁶⁶University of Kansas Medical Center, Kansas, USA. ²⁶⁷The Montreal Children's Hospital, Montreal, Canada. ²⁶⁸Vancouver General Hospital, Vancouver, Canada. ²⁶⁹Ospedale San Gerardo, Monza, Italy. ²⁷⁰Hôpital Foch, Suresnes, France. ²⁷¹Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada. ²⁷²Bon Secours Hospital, Cork, Ireland. ²⁷³Hospital Verge de la Cinta, Tortosa, Spain. ²⁷⁴Hospital Escola da Universidade Federal de Pelotas, Pelotas, Brazil. ²⁷⁵Hôpital Albert Calmette, Lille, France. ²⁷⁶Saiseikai Senri Hospital, Tochigi, Japan. ²⁷⁷Manipal Hospital Whitefield, Bangalore, India. ²⁷⁸Centre Hospitalier de Pau, Pau, France. ²⁷⁹Hôpital privé d'Antony, Antony, France. ²⁸⁰São João Hospital Centre, Porto, Portugal. ²⁸¹San Pedro de Alcantara Hospital, Cáceres, Spain. ²⁸²Beth Israel Deaconess Medical Center, Boston, USA. ²⁸³Ochsner Clinic Foundation, New Orleans, USA. ²⁸⁴Department of Internal Medicine No2, Lugansk State Medical University, Lugansk, Ukraine. ²⁸⁵Klinikum Passau, Germant, Germany. ²⁸⁶Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates. ²⁸⁷University of Padua, Padua, Italy. ²⁸⁸Children's Hospital of Eastern Ontario, Ottawa, Canada. ²⁸⁹Mater Misericordiae University, Dublin, Ireland. ²⁹⁰Centre Hospitalier Henri Duffaut, Avignon, France. ²⁹¹The Baruch Padeh Medical Center Poriya, Tiberias, Israel. ²⁹²Lankenau Institute of Medical Research, Wynnewood, USA. ²⁹³The Hospital for Sick Children (SickKids), Toronto, Canada. ²⁹⁴University Hospital of Tubingen, Tubingen, Germany. ²⁹⁵CUB-Hôpital Erasme, Anderlecht, Belgium. ²⁹⁶Permai Hospital, Johor, Malaysia. ²⁹⁷Schools of Medicine, University of Michigan, Ann Arbor, USA. ²⁹⁸Hospital Garcia de Orta, Almada, Portugal. ²⁹⁹Department of Infectious Diseases, Institut Hospital del Mar d'Investigacions Médiques (IMIM), Infectious Pathology and Antimicrobial Research Group (IPAR), Hospital del Mar, Universitat Autònoma de Barcelona (UAB), CEXS-Universitat Pompeu Fabra, Barcelona, Spain. ³⁰⁰Wexford General Hospital, Wexford, Ireland. ³⁰¹Baylor Scott, Dallas, USA. ³⁰²Clinica Alemana DeSantiago, Santiago, Chile. ³⁰³Centre Hospitalier du Pays d'Aix, Aix-en-Provence, France. ³⁰⁴Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK. ³⁰⁵Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK. ³⁰⁶Centre Hospitalier Universitaire Ambroise-Paré, Boulogne-Billancourt, France. ³⁰⁷Roslin Institute, University of Edinburgh, Easter Bush, EH25 9RG Edinburgh, UK. ³⁰⁸Grigore T, Popa University of Medicine and Pharmacy, Bucharest, Romania. ³⁰⁹Erasmus Medical Centre, Rotterdam, The Netherlands. ³¹⁰University Children's Hospital, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. ³¹¹Hospital de Curry Cabral - Internal Medicine, Lisbon, Portugal. ³¹²Centre Hospitalier Universitaire Toulouse (Larrey), Toulouse, France. ³¹³Hospital de Amor, Sao Paulo, Brazil. ³¹⁴Middlemore Hospital (Canties Manukan Health), Otahuhu, New Zealand. ³¹⁵Centre Hospitalier de Soissons, Soissons, France. ³¹⁶Red Deer Regional Hospital, Red Deer, Canada. ³¹⁷Big Data Institute, Department of Medicine, University of Oxford, Nuffield, Oxford, UK. ³¹⁸McLeod Healthcare System, Florence, USA. ³¹⁹Providence Saint John's Health Centre, Santa Monica, USA. ³²⁰Kluang Hospital, Johor, Malaysia. ³²¹Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, NIHR Health Protection Research Unit, University of Liverpool, Liverpool, UK. ³²²Kintampo Health Research Centre, Kintampo, Ghana. ³²³AZ Maria Middelares, Gent, Belgium. ³²⁴Drammen Hospital, Drammen, Norway. ³²⁵Universidad de Las Américas, Quito, Ecuador. ³²⁶Lancaster General Health, Pennsylvania, USA. ³²⁷PICU Saiful Anwar Hospital, Malang, Indonesia. ³²⁸Nagoya University Hospital,
Nagoya, Japan. ³²⁹Long COVID India - Terna Specialty Hospital and Research Centre, Mumbai, India. ³³⁰Centre Hospitalier Le Mans, Le Mans, France. ³³¹Medical Research Council, University of Glasgow Centre for Virus Research, Glasgow, UK. ³³²Sultanah Bahiyah Hospital, Kedah, Malaysia. ³³³Tuanku Ja'afar, Negeri Sembilan, Seremban, Malaysia. ³³⁴University of Alabama at Birmingham Hospital, Birmingham, USA. ³³⁵Hokkaido University Hospital, Hokkaido, Japan. ³³⁶Fukuoka University, Fukuoka, Japan. ³³⁷US NHLBI PETAL Network, Boston, USA. ³³⁸University of California - San Francisco (UCSF), San Francisco, USA. ³³⁹St. Joseph's Healthcare Hamilton, Hamilton, Canada. ³⁴⁰Virology Reference Department, National Infection Service, Public Health England, Colindale Avenue, London, UK. ³⁴¹La Paz Hospital, Madrid, Spain. ³⁴²Centre Hospitalier Annecy Genevois, Annecy, France. ³⁴³Hippokration Hospital, Thessaloniki, Greece. ³⁴⁴RSUP Fatmawati, South Jakarta, Indonesia. ³⁴⁵Hiroshima University, Hiroshima, Japan. ³⁴⁶Mie University Hospital, Tsu, Japan. ³⁴⁷Hospital Aleman, Buenos Aires, Argentina. ³⁴⁸Oxford University (ISARIC4C), Oxford, UK. ³⁴⁹Mills Memorial Hospital, Terrace, Canada. ³⁵⁰Raja Perempuan Zainab II Hospital, Kelantan, Malaysia. ³⁵¹Hospital Nuestra Señora de Gracia, Zaragoza, Spain. ³⁵²Kyungpook National University Hospital, Daegu, South Korea. ³⁵³Consortium IMGEN, Piaseczno, Poland. ³⁵⁴Tawau Hospital, Sabah, Malaysia. ³⁵⁵Melaka Hospital, Melaka, Malaysia. ³⁵⁶Princess Margaret Hospital, Kwai Hung, China. ³⁵⁷Nationwide Children's Hospital, Columbus, USA. ³⁵⁸Washington University in St. Louis, St Louis, Missouri, USA. ³⁵⁹Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, UK. ³⁶⁰University of Oklahoma Health Sciences Center, Oklahoma, USA. ³⁶¹Groupe Hospitalier Diaconesses Croix Saint-Simon, Paris, France. ³⁶²Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. ³⁶³Unity Health Toronto, Toronto, Canada. ³⁶⁴McMaster University, Hamilton, Canada. ³⁶⁵Lahad Datu Hospital, Sabah, Malaysia. ³⁶⁶Department of Pharmacology, University of Liverpool, Liverpool, UK. ³⁶⁷University Hospital of North Norway, Tromso, Norway. ³⁶⁸Keimyung University Dong San Hospital, Daegu, South Korea. ³⁶⁹Kimitsu Chuo Hospital, Chiba, Japan. ³⁷⁰Nuffield Department of Medicine, Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, UK. ³⁷¹Obihiro-Kosei General Hospital, Obihiro, Japan. ³⁷²Marmara University Hospital, Istanbul, Turkey. ³⁷³University Health Network, Toronto, Canada. ³⁷⁴Apollo Hospitals Chennai, Chennai, India. ³⁷⁵Hartford HealthCare, Hartford, USA. ³⁷⁶University Airlangga Hospital (Paediatric), Surabaya, Indonesia. ³⁷⁷Royal Alexandra Hospital, Edmonton, Canada. ³⁷⁸Centre Hospitalier Alpes-Leman, Contamine-sur-Arve, France. ³⁷⁹Centre Hospitalier Universitaire Toulouse (Rangueil), Toulouse, France. ³⁸⁰Prof Dr R. D. Kandou Central Hospital (Adult), Manado, Indonesia. ³⁸¹Université de Paris, Paris, France. ³⁸²Centre Hospitalier Universitaire de Nîmes, Nîmes, France. ³⁸³The Roslin Institute, University of Edinburgh, Edinburgh, UK. ³⁸⁴Wellington Regional Hospital, Wellington, New Zealand. ³⁸⁵University of Alberta Adult ICU, Edmonton, Canada. ³⁸⁶Centre Hospitalier Universitaire de Montpellier, Montpellier, France. ³⁸⁷Centre Hospitalier Universitaire de Poitiers, Poitiers, France. ³⁸⁸Queen Elizabeth Hospital, Sabah, Malaysia. ³⁸⁹Severance Hospital, Seoul, South Korea. ³⁹⁰Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Centre for Health Informatics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK. ³⁹¹Hôpital Henri-Mondor, Créteil, France. ³⁹²University Institute of Cardiology and Respirology, Quebec, Canada. ³⁹³Sultanah Nur Zahirah Hospital, Terengganu, Malaysia. ³⁹⁴Centre Hospitalier Universitaire Gabriel Montpied, Clermont-Ferrand, France. ³⁹⁵Institute of TB and Lung Diseases, Warsaw, Poland. ³⁹⁶Waitemata District Health Board, Auckland, New Zealand. ³⁹⁷Nottingham University Hospitals NHS Trust, Nottingham, UK. ³⁹⁸Angeles University Foundation Medical Center, Angeles, Philippines. ³⁹⁹Malawi-Liverpool Wellcome Trust, Blantyre, Malawi. ⁴⁰⁰Saiseikai Utsunomiya Hospital, Tochigi, Japan. ⁴⁰¹University of Florida, Gainesville, USA. ⁴⁰²Instituto Nacional del Niño San Borja, Lima, Peru. ⁴⁰³Hospital de Clínicas, Buenos Aires, Argentina. ⁴⁰⁴Hospital Emergencia Ate Vitarte, Lima, Peru. ⁴⁰⁵Port Macquarie Base Hospital, Port Macquarie, Australia. ⁴⁰⁶Klinik und Poliklinik für Innere Medizin II, University Hospital Regensburg, Kiel, Germany. ⁴⁰⁷William Osler Health Sciences System - Etobicoke General Hospital, Toronto, Canada. ⁴⁰⁸Prof Dr R. D. Kandou Central Hospital (Paediatric), Manado, Indonesia. ⁴⁰⁹Hôpital Louis-Mourier, Colombes, France. ⁴¹⁰Hospital Vila Franca de Xira, Lisbon, Portugal. ⁴¹¹Alberta Children's Hospital, Calgary, Canada. ⁴¹²Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organisation (MICRO), St. James's Hospital, Dublin, Ireland. ⁴¹³Centre Hospitalier de Colmar, Colmar, France. ⁴¹⁴Emergency Department, Hospital, Universitario La Paz - IdiPAZ, Madrid, Spain. ⁴¹⁵Kingston Health Sciences Centre, Kingston, Canada. ⁴¹⁶International Islamic University Malaysia Medical Centre (IIUMMC), Pahang, Malaysia. ⁴¹⁷Waikato Hospital, Hamilton, New Zealand. ⁴¹⁸Auckland City Hospital, (DCCM 82), Auckland, New Zealand. ⁴¹⁹Mount Sinai Hospital, Toronto, Canada. ⁴²⁰London Health Sciences Centre, London, Canada. ⁴²¹Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. ⁴²²Centre Hospitalier de Cahors, Cahors, France. ⁴²³Institute of Genetics and Molecular Medicine, MRC Human Genetics Unit, MRC, University of Edinburgh, Edinburgh, UK. ⁴²⁴Centre Hospitalier Universitaire Rouen (Hôpital Charles Nicolle), Rouen, France. ⁴²⁵Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK. ⁴²⁶Hospital Clinic, Barcelona, Spain. ⁴²⁷Tengku Ampuan Afzan Hospital, Pahang, Malaysia. ⁴²⁸Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, Care for Long Term Conditions Division, King's College London, London, UK. ⁴²⁹Faculty of Medicine, University of British Columbia, Vancouver, Canada. ⁴³⁰Baystate MC, Springfield, USA. ⁴³¹University Hospital Northern British Columbia, Prince George, Canada. ⁴³²St-Pierre University Hospital, Brussels, Belgium. ⁴³³Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Kota Depok, Indonesia. ⁴³⁴Queen Mary Hospital, Pok Fu Lam, China. ⁴³⁵Hôpital Pellegrin, Bordeaux, France. ⁴³⁶Irish Critical Care Critical Clinical Trials Network, Dublin, Ireland, UK. ⁴³⁷Siriraj Piyamaharajkarun Hospital (SiPH), Bangkok, Thailand. ⁴³⁸Oregon Health, Salem, USA. ⁴³⁹Cleveland Clinic, Abu Dhabi, United Arab Emirates. ⁴⁴⁰Division of Infection and Immunity, University College London, London, UK. ⁴⁴¹Department of Children's Infectious Diseases, Warsaw, Poland. ⁴⁴²Dr Sardjito Government Hospital (Paediatric), Yogyakarta, Indonesia. ⁴⁴³RSUD Pasar Minggu, South Jakarta, Indonesia. ⁴⁴⁴Mercy Hospital, Cork, Ireland. ⁴⁴⁵Legacy Emanuel Medical Center, Portland, USA. ⁴⁴⁶Kyung Pook National University Chilgok Hospital, Daegu, South Korea. ⁴⁴⁷Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan. ⁴⁴⁸Instituto do Coração da Universidade de São Paulo (INCOR), São Paulo, Brazil. ⁴⁴⁹Misericordia Community Hospital, Edmonton, Canada. ⁴⁵⁰Department of Medical Microbiology and Infection Control,
Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands. ⁴⁵¹Joseph Brant Hospital, Burlington, Canada.

⁴⁵²National Heart and Lung Institute, Imperial College London, London, UK. ⁴⁵³Tufts Medical Centre, Boston, USA.

⁴⁵⁴Mayo Clinic School of Medicine, Arizona, USA. ⁴⁵⁵All India Institute of Medical Sciences (AIIMS), Rishikesh, India.

⁴⁵⁶Hospital General San Francisco, Quito, Ecuador. ⁴⁵⁷Auckland City Hospital (CVICU), Auckland, New Zealand.

⁴⁵⁸Hospital Beatriz Ângelo, Loures, Portugal. ⁴⁵⁹Niagara Health, Niagara, Canada. ⁴⁶⁰Centre Hospitalier de Périgueux, Périgueux, France. ⁴⁶¹University Hospital Ostrava, Ostrava-Poruba, Czechia. ⁴⁶²Humber River Hospital, Toronto, Canada. ⁴⁶³Maastricht University Medical Centre, Maastricht, The Netherlands. ⁴⁶⁴North Estonia Medical Centre, Tallin, Estonia. ⁴⁶⁵RSUD Dr. Soetomo, Surabaya, Indonesia. ⁴⁶⁶Baylor University Medical Centre, Dallas, USA. ⁴⁶⁷Apollo Hospitals Chennai, Chennai, Tamil Nadu, India. ⁴⁶⁸Ministry of Health, and D'Or Institute of Research and Education (IDOR), National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation (INIFIOCRUZ), Rio de Janeiro, Gaspar Viana Pavilion, Brazil. ⁴⁶⁹Network for Improving Critical care Systems and Training, Colombo, Sri Lanka. ⁴⁷⁰Clinic of Anesthesiology and Intensive Care, North Estonia Medical Centre, Tallinn, Estonia. ⁴⁷¹Hospital de Abrantes - ICU, Abrantes, Portugal. ⁴⁷²Hôpital Européen Marseille, Marseille, France.

⁴⁷³Centre Hospitalier Agen-Nérac, Agen, France. ⁴⁷⁴Vall d'Hebron Institute of Research, Barcelona, Spain.

⁴⁷⁵University of Manitoba, Manitoba, Canada. ⁴⁷⁶Universidad de La Sabana, Chia, Colombia. ⁴⁷⁷The Center for Diagnosis, Santo Domingo, Dominican Republic. ⁴⁷⁸CHU Carêmeau, Nimes, France. ⁴⁷⁹Royal Melbourne Hospital, Melbourne, Australia. ⁴⁸⁰Hôpital Laënnec - site de Quimper, Quimper, France. ⁴⁸¹Fundación Cardiovascular de Colombia, Floridablanca, Colombia. ⁴⁸²Hospital Universitari Sant Joan D'Alacant, Alicante, Spain. ⁴⁸³Department of Pediatrics and Virology, St Mary's Medical School Bldg, Imperial College London, London, UK. ⁴⁸⁴Department of Infectious Diseases I, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania. ⁴⁸⁵Centro Hospitalar Universitário do Algarve, Portimão, Portugal. ⁴⁸⁶RSPI Prof Dr Sulianti Saroso, Jakarta, Indonesia. ⁴⁸⁷The Heart Hospital Baylor Plano, Plano, USA. ⁴⁸⁸Gelre Hospitals, Zutphen, The Netherlands. ⁴⁸⁹Krankenhaus Barmherzige Br, Regensburg, Germany. ⁴⁹⁰Baylor AllSaints Medical Centre, Fort Worth, USA. ⁴⁹¹Sozialmedizinisches Zentrum Sud, Vienna, Austria. ⁴⁹²Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK. ⁴⁹³Centro Hospitalar de Leiria, Leiria, Portugal. ⁴⁹⁴Institute of Translational Medicine, University of Liverpool, Liverpool, Merseyside, UK. ⁴⁹⁵Tohoku University, Sendai, Japan. ⁴⁹⁶Hyogo Prefectural Kakogawa Medical Center, Hyogo, Japan. ⁴⁹⁷Tokyo Metropolitan Tama Medical Center, Tokyo, Japan.

⁴⁹⁸Pamela Youde Nethersole Eastern Hospital, Chai Wan, China. ⁴⁹⁹Grand River Hospital, Kitchener, Canada.

⁵⁰⁰Hospital Sungai Buloh, Ministry of Health, Selangor, Malaysia. ⁵⁰¹Seoul National University Bundang Hospital, Seoul, South Korea. ⁵⁰²Hospital Universitário Clementino Fraga Filho, Rio de Janeiro, Brazil. ⁵⁰³Baerum Sykehus, Gjettum, Norway. ⁵⁰⁴National Institute for Infectious Diseases Matei Bals, Bucharest, Romania. ⁵⁰⁵Carol Davila University of Medicine and Pharmacy, Bucharest, Romania. ⁵⁰⁶Division of Structural Biology, The Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK. ⁵⁰⁷Sturgeon Community Hospital, St Albert, Canada. ⁵⁰⁸Department of Medicine, University of Cambridge, Cambridge, Cambridgeshire, UK.

⁵⁰⁹University Hospital in Krakow, Krakow, Poland. ⁵¹⁰Centre Hospitalier Universitaire Grenoble-Alpes FU, Grenoble, France. ⁵¹¹Hospital das Clínicas da Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil. ⁵¹²Kyoto Medical Centre, Kyoto, Japan. ⁵¹³Yokohama City University Medical Center, Yokohama, Japan.

⁵¹⁴Fatmawati Hospital, Jakarta, Indonesia. ⁵¹⁵Virus Reference Department, National Infection Service, Blood Borne Virus Unit, Public Health England, London, UK. ⁵¹⁶Complexo Hospitalar Dr. Clementino Fraga, João Pessoa city, Brazil. ⁵¹⁷Centre Hospitalier Louis Raffalli, Manosque, France. ⁵¹⁸University of Nebraska Medical Center, Omaha, USA. ⁵¹⁹Division of Critical Care, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa.

⁵²⁰Clínica Internacional, Lima, Peru. ⁵²¹Albury Wodonga Health, Albury, Australia. ⁵²²Hôpital Robert-Debré AP-HP, Paris, France. ⁵²³Dunedin Public Hospital, Dunedin, New Zealand. ⁵²⁴ADZR, Amsterdam, The Netherlands.

⁵²⁵Meander Medical Centre, Amersfoort, The Netherlands. ⁵²⁶Adrz, Goes, The Netherlands. ⁵²⁷Noordwest-Ziekenhuisgroep, DenHelder, The Netherlands. ⁵²⁸Grey Nun's Community Hospital, Edmonton, Canada. ⁵²⁹Beatrix ziekenhuis, Gorinchem, The Netherlands. ⁵³⁰Royal Columbian Hospital, Vancouver, Canada. ⁵³¹Department of Intensive Care, Franciscus Gasthuis, Rotterdam, The Netherlands. ⁵³²Queen Elizabeth Hospital, Yau Ma Tei, China.

⁵³³Kyoto Prefectural University of Medicine, Kyoto, Japan. ⁵³⁴Kouritu Tousei Hospital, Seto City, Japan. ⁵³⁵MedStar Washington Hospital Centre, Washington, USA. ⁵³⁶Clinica Pasteur National - University of Comahue, Neuquén, Argentina. ⁵³⁷Sultanah Aminah Hospital, Johor, Malaysia. ⁵³⁸National Infection Service, Public Health England, London, UK. ⁵³⁹Mazankowski Heart Institute, Edmonton, Canada.