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1. Accelerated immune ageing is associated with COVID-19 disease severity

Author

Janet M. Lord, Tonny Veenith, Jack Sullivan, Archana Sharma-Oates, Alex G. Richter, Neil J. Greening, Hamish J. C. McAuley, Rachael A. Evans, Paul Moss, Shona C. Moore, Lance Turtle, Nandan Gautam, Ahmed Gilani, Manan Bajaj, Louise V. Wain, Christopher Brightling, Betty Raman, Michael Marks, Amisha Singapuri, Omer Elneima, Peter J. M. Openshaw, Niharika A. Duggal, on behalf of the PHOSP-COVID Study collaborative group, and ISARIC4C investigators

Subjects
Immunologic diseases. Allergy, RC581-607, Geriatrics, RC952-954.6
Abstract

Abstract Background The striking increase in COVID-19 severity in older adults provides a clear example of immunesenescence, the age-related remodelling of the immune system. To better characterise the association between convalescent immunesenescence and acute disease severity, we determined the immune phenotype of COVID-19 survivors and non-infected controls. Results We performed detailed immune phenotyping of peripheral blood mononuclear cells isolated from 103 COVID-19 survivors 3–5 months post recovery who were classified as having had severe (n = 56; age 53.12 ± 11.30 years), moderate (n = 32; age 52.28 ± 11.43 years) or mild (n = 15; age 49.67 ± 7.30 years) disease and compared with age and sex-matched healthy adults (n = 59; age 50.49 ± 10.68 years). We assessed a broad range of immune cell phenotypes to generate a composite score, IMM-AGE, to determine the degree of immune senescence. We found increased immunesenescence features in severe COVID-19 survivors compared to controls including: a reduced frequency and number of naïve CD4 and CD8 T cells (p

Published
2024
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2. Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

Author

Benedict D. Michael, Cordelia Dunai, Edward J. Needham, Kukatharmini Tharmaratnam, Robyn Williams, Yun Huang, Sarah A. Boardman, Jordan J. Clark, Parul Sharma, Krishanthi Subramaniam, Greta K. Wood, Ceryce Collie, Richard Digby, Alexander Ren, Emma Norton, Maya Leibowitz, Soraya Ebrahimi, Andrew Fower, Hannah Fox, Esteban Tato, Mark A. Ellul, Geraint Sunderland, Marie Held, Claire Hetherington, Franklyn N. Egbe, Alish Palmos, Kathy Stirrups, Alexander Grundmann, Anne-Cecile Chiollaz, Jean-Charles Sanchez, James P. Stewart, Michael Griffiths, Tom Solomon, Gerome Breen, Alasdair J. Coles, Nathalie Kingston, John R. Bradley, Patrick F. Chinnery, Jonathan Cavanagh, Sarosh R. Irani, Angela Vincent, J. Kenneth Baillie, Peter J. Openshaw, Malcolm G. Semple, ISARIC4C Investigators, COVID-CNS Consortium, Leonie S. Taams, and David K. Menon

Subjects
Science
Abstract

Abstract To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely.

Published
2023
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3. Evaluation of pragmatic oxygenation measurement as a proxy for Covid-19 severity

Author

Maaike C. Swets, Steven Kerr, James Scott-Brown, Adam B. Brown, Rishi Gupta, Jonathan E. Millar, Enti Spata, Fiona McCurrach, Andrew D. Bretherick, Annemarie Docherty, David Harrison, Kathy Rowan, Neil Young, ISARIC4C Investigators, Geert H. Groeneveld, Jake Dunning, Jonathan S. Nguyen-Van-Tam, Peter Openshaw, Peter W. Horby, Ewen Harrison, Natalie Staplin, Malcolm G. Semple, Nazir Lone, and J. Kenneth Baillie

Subjects
Science
Abstract

Abstract Choosing optimal outcome measures maximizes statistical power, accelerates discovery and improves reliability in early-phase trials. We devised and evaluated a modification to a pragmatic measure of oxygenation function, the $$S/F$$ S / F ratio. Because of the ceiling effect in oxyhaemoglobin saturation, $$S/F$$ S / F ratio ceases to reflect pulmonary oxygenation function at high $${S}_{p}{O}_{2}$$ S p O 2 values. We found that the correlation of $$S/F$$ S / F with the reference standard ( $${P}_{a}{O}_{2}$$ P a O 2 / $${F}_{I}{O}_{2}$$ F I O 2 ratio) improves substantially when excluding $${S}_{p}{O}_{2} > 0.94$$ S p O 2 > 0.94 and refer to this measure as $$S/{F}_{94}$$ S / F 94 . Using observational data from 39,765 hospitalised COVID-19 patients, we demonstrate that $$S/{F}_{94}$$ S / F 94 is predictive of mortality, and compare the sample sizes required for trials using four different outcome measures. We show that a significant difference in outcome could be detected with the smallest sample size using $$S/{F}_{94}$$ S / F 94 . We demonstrate that $$S/{F}_{94}$$ S / F 94 is an effective intermediate outcome measure in COVID-19. It is a non-invasive measurement, representative of disease severity and provides greater statistical power.

Published
2023
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4. Author Correction: Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

Author

Benedict D. Michael, Cordelia Dunai, Edward J. Needham, Kukatharmini Tharmaratnam, Robyn Williams, Yun Huang, Sarah A. Boardman, Jordan J. Clark, Parul Sharma, Krishanthi Subramaniam, Greta K. Wood, Ceryce Collie, Richard Digby, Alexander Ren, Emma Norton, Maya Leibowitz, Soraya Ebrahimi, Andrew Fower, Hannah Fox, Esteban Tato, Mark A. Ellul, Geraint Sunderland, Marie Held, Claire Hetherington, Franklyn N. Egbe, Alish Palmos, Kathy Stirrups, Alexander Grundmann, Anne-Cecile Chiollaz, Jean-Charles Sanchez, James P. Stewart, Michael Griffiths, Tom Solomon, Gerome Breen, Alasdair J. Coles, Nathalie Kingston, John R. Bradley, Patrick F. Chinnery, Jonathan Cavanagh, Sarosh R. Irani, Angela Vincent, J. Kenneth Baillie, Peter J. Openshaw, Malcolm G. Semple, ISARIC4C Investigators, COVID-CNS Consortium, Leonie S. Taams, and David K. Menon

Subjects
Science
Published
2024
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5. The P323L substitution in the SARS-CoV-2 polymerase (NSP12) confers a selective advantage during infection

Author

Hannah Goldswain, Xiaofeng Dong, Rebekah Penrice-Randal, Muhannad Alruwaili, Ghada T. Shawli, Tessa Prince, Maia Kavanagh Williamson, Jayna Raghwani, Nadine Randle, Benjamin Jones, I’ah Donovan-Banfield, Francisco J. Salguero, Julia A. Tree, Yper Hall, Catherine Hartley, Maximilian Erdmann, James Bazire, Tuksin Jearanaiwitayakul, Malcolm G. Semple, Peter J. M. Openshaw, J. Kenneth Baillie, ISARIC4C Investigators, Stevan R. Emmett, Paul Digard, David A. Matthews, Lance Turtle, Alistair C. Darby, Andrew D. Davidson, Miles W. Carroll, and Julian A. Hiscox

Subjects
SARS-CoV-2, Evolution, Selection, Spike protein, Polymerase, NSP12, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background The mutational landscape of SARS-CoV-2 varies at the dominant viral genome sequence and minor genomic variant population. During the COVID-19 pandemic, an early substitution in the genome was the D614G change in the spike protein, associated with an increase in transmissibility. Genomes with D614G are accompanied by a P323L substitution in the viral polymerase (NSP12). However, P323L is not thought to be under strong selective pressure. Results Investigation of P323L/D614G substitutions in the population shows rapid emergence during the containment phase and early surge phase during the first wave. These substitutions emerge from minor genomic variants which become dominant viral genome sequence. This is investigated in vivo and in vitro using SARS-CoV-2 with P323 and D614 in the dominant genome sequence and L323 and G614 in the minor variant population. During infection, there is rapid selection of L323 into the dominant viral genome sequence but not G614. Reverse genetics is used to create two viruses (either P323 or L323) with the same genetic background. L323 shows greater abundance of viral RNA and proteins and a smaller plaque morphology than P323. Conclusions These data suggest that P323L is an important contribution in the emergence of variants with transmission advantages. Sequence analysis of viral populations suggests it may be possible to predict the emergence of a new variant based on tracking the frequency of minor variant genomes. The ability to predict an emerging variant of SARS-CoV-2 in the global landscape may aid in the evaluation of medical countermeasures and non-pharmaceutical interventions.

Published
2023
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6. The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

Author

Gwenan M. Knight, Thi Mui Pham, James Stimson, Sebastian Funk, Yalda Jafari, Diane Pople, Stephanie Evans, Mo Yin, Colin S. Brown, Alex Bhattacharya, Russell Hope, Malcolm G. Semple, ISARIC4C Investigators, CMMID COVID-19 Working Group, Jonathan M. Read, Ben S. Cooper, and Julie V. Robotham

Subjects
COVID-19, SARS-CoV-2, Nosocomial transmission, Mathematical modelling, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background SARS-CoV-2 is known to transmit in hospital settings, but the contribution of infections acquired in hospitals to the epidemic at a national scale is unknown. Methods We used comprehensive national English datasets to determine the number of COVID-19 patients with identified hospital-acquired infections (with symptom onset > 7 days after admission and before discharge) in acute English hospitals up to August 2020. As patients may leave the hospital prior to detection of infection or have rapid symptom onset, we combined measures of the length of stay and the incubation period distribution to estimate how many hospital-acquired infections may have been missed. We used simulations to estimate the total number (identified and unidentified) of symptomatic hospital-acquired infections, as well as infections due to onward community transmission from missed hospital-acquired infections, to 31st July 2020. Results In our dataset of hospitalised COVID-19 patients in acute English hospitals with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired. We estimated that only 30% (range across weeks and 200 simulations: 20–41%) of symptomatic hospital-acquired infections would be identified, with up to 15% (mean, 95% range over 200 simulations: 14.1–15.8%) of cases currently classified as community-acquired COVID-19 potentially linked to hospital transmission. We estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200–16,400) or 20.1% (19.2–20.7%) of all identified hospitalised COVID-19 cases. Conclusions Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the “first wave” in England, but less than 1% of all infections in England. Using time to symptom onset from admission for inpatients as a detection method likely misses a substantial proportion (> 60%) of hospital-acquired infections.

Published
2022
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7. SARS-CoV-2 antibody responses associate with sex, age and disease severity in previously uninfected people admitted to hospital with COVID-19: An ISARIC4C prospective study

Author

Eleanor Parker, Jordan Thomas, Kelly J. Roper, Samreen Ijaz, Tansy Edwards, Federica Marchesin, Ksenia Katsanovskaja, Lauren Lett, Christopher Jones, Hayley E. Hardwick, Chris Davis, Elen Vink, Sarah E. McDonald, Shona C. Moore, Steve Dicks, Keerthana Jegatheesan, Nicola J. Cook, Joshua Hope, Peter Cherepanov, Myra O. McClure, J. Kenneth Baillie, Peter J. M. Openshaw, Lance Turtle, Antonia Ho, Malcolm G. Semple, William A. Paxton, Richard S. Tedder, Georgios Pollakis, and ISARIC4C Investigators

Subjects
SARS-CoV-2, immunology, COVID-19, virus, disease, serology, Immunologic diseases. Allergy, RC581-607
Abstract

The SARS-CoV-2 pandemic enables the analysis of immune responses induced against a novel coronavirus infecting immunologically naïve individuals. This provides an opportunity for analysis of immune responses and associations with age, sex and disease severity. Here we measured an array of solid-phase binding antibody and viral neutralising Ab (nAb) responses in participants (n=337) of the ISARIC4C cohort and characterised their correlation with peak disease severity during acute infection and early convalescence. Overall, the responses in a Double Antigen Binding Assay (DABA) for antibody to the receptor binding domain (anti-RBD) correlated well with IgM as well as IgG responses against viral spike, S1 and nucleocapsid protein (NP) antigens. DABA reactivity also correlated with nAb. As we and others reported previously, there is greater risk of severe disease and death in older men, whilst the sex ratio was found to be equal within each severity grouping in younger people. In older males with severe disease (mean age 68 years), peak antibody levels were found to be delayed by one to two weeks compared with women, and nAb responses were delayed further. Additionally, we demonstrated that solid-phase binding antibody responses reached higher levels in males as measured via DABA and IgM binding against Spike, NP and S1 antigens. In contrast, this was not observed for nAb responses. When measuring SARS-CoV-2 RNA transcripts (as a surrogate for viral shedding) in nasal swabs at recruitment, we saw no significant differences by sex or disease severity status. However, we have shown higher antibody levels associated with low nasal viral RNA indicating a role of antibody responses in controlling viral replication and shedding in the upper airway. In this study, we have shown discernible differences in the humoral immune responses between males and females and these differences associate with age as well as with resultant disease severity.

Published
2023
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8. Outcome of COVID-19 in hospitalised immunocompromised patients: An analysis of the WHO ISARIC CCP-UK prospective cohort study.

Author

Lance Turtle, Mathew Thorpe, Thomas M Drake, Maaike Swets, Carlo Palmieri, Clark D Russell, Antonia Ho, Stephen Aston, Daniel G Wootton, Alex Richter, Thushan I de Silva, Hayley E Hardwick, Gary Leeming, Andy Law, Peter J M Openshaw, Ewen M Harrison, ISARIC4C investigators, J Kenneth Baillie, Malcolm G Semple, and Annemarie B Docherty

Subjects
Medicine
Abstract

BackgroundImmunocompromised patients may be at higher risk of mortality if hospitalised with Coronavirus Disease 2019 (COVID-19) compared with immunocompetent patients. However, previous studies have been contradictory. We aimed to determine whether immunocompromised patients were at greater risk of in-hospital death and how this risk changed over the pandemic.Methods and findingsWe included patients > = 19 years with symptomatic community-acquired COVID-19 recruited to the ISARIC WHO Clinical Characterisation Protocol UK prospective cohort study. We defined immunocompromise as immunosuppressant medication preadmission, cancer treatment, organ transplant, HIV, or congenital immunodeficiency. We used logistic regression to compare the risk of death in both groups, adjusting for age, sex, deprivation, ethnicity, vaccination, and comorbidities. We used Bayesian logistic regression to explore mortality over time. Between 17 January 2020 and 28 February 2022, we recruited 156,552 eligible patients, of whom 21,954 (14%) were immunocompromised. In total, 29% (n = 6,499) of immunocompromised and 21% (n = 28,608) of immunocompetent patients died in hospital. The odds of in-hospital mortality were elevated for immunocompromised patients (adjusted OR 1.44, 95% CI [1.39, 1.50], p < 0.001). Not all immunocompromising conditions had the same risk, for example, patients on active cancer treatment were less likely to have their care escalated to intensive care (adjusted OR 0.77, 95% CI [0.7, 0.85], p < 0.001) or ventilation (adjusted OR 0.65, 95% CI [0.56, 0.76], p < 0.001). However, cancer patients were more likely to die (adjusted OR 2.0, 95% CI [1.87, 2.15], p < 0.001). Analyses were adjusted for age, sex, socioeconomic deprivation, comorbidities, and vaccination status. As the pandemic progressed, in-hospital mortality reduced more slowly for immunocompromised patients than for immunocompetent patients. This was particularly evident with increasing age: the probability of the reduction in hospital mortality being less for immunocompromised patients aged 50 to 69 years was 88% for men and 83% for women, and for those >80 years was 99% for men and 98% for women. The study is limited by a lack of detailed drug data prior to admission, including steroid doses, meaning that we may have incorrectly categorised some immunocompromised patients as immunocompetent.ConclusionsImmunocompromised patients remain at elevated risk of death from COVID-19. Targeted measures such as additional vaccine doses, monoclonal antibodies, and nonpharmaceutical preventive interventions should be continually encouraged for this patient group.Trial registrationISRCTN 66726260.

Published
2023
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9. Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

Author

Quentin J. Leclerc, Naomi M. Fuller, Ruth H. Keogh, Karla Diaz-Ordaz, Richard Sekula, Malcolm G. Semple, ISARIC4C Investigators, CMMID COVID-19 Working Group, Katherine E. Atkins, Simon R. Procter, and Gwenan M. Knight

Subjects
COVID-19, SARS-CoV-2, Hospitalisation, Length of stay, Bed occupancy, Bed pathway, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. Methods We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. Results In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. Conclusions We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. Trial registration The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

Published
2021
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10. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol : development and validation of the 4C Mortality Score

Author

ISARIC4C investigators, Knight, Stephen R, Ho, Antonia, Pius, Riinu, Buchan, Iain, Carson, Gail, Drake, Thomas M, Dunning, Jake, Fairfield, Cameron J, Gamble, Carrol, Green, Christopher A, Gupta, Rishi, Halpin, Sophie, Hardwick, Hayley E, Holden, Karl A, Horby, Peter W, Jackson, Clare, Mclean, Kenneth A, Merson, Laura, Nguyen-Van-Tam, Jonathan S, Norman, Lisa, Noursadeghi, Mahdad, Olliaro, Piero L, Pritchard, Mark G, Russell, Clark D, Shaw, Catherine A, Sheikh, Aziz, Solomon, Tom, Sudlow, Cathie, Swann, Olivia V, Turtle, Lance CW, Openshaw, Peter JM, Baillie, J Kenneth, Semple, Malcolm G, Docherty, Annemarie B, and Harrison, Ewen M

Published
2020

11. Clinical characteristics of children and young people admitted to hospital with covid-19 in United Kingdom : prospective multicentre observational cohort study

Author

ISARIC4C Investigators, Swann, Olivia V, Holden, Karl A, Turtle, Lance, Pollock, Louisa, Fairfield, Cameron J, Drake, Thomas M, Seth, Sohan, Egan, Conor, Hardwick, Hayley E, Halpin, Sophie, Girvan, Michelle, Donohue, Chloe, Pritchard, Mark, Patel, Latifa B, Ladhani, Shamez, Sigfrid, Louise, Sinha, Ian P, Olliaro, Piero L, Nguyen-Van-Tam, Jonathan S, Horby, Peter W, Merson, Laura, Carson, Gail, Dunning, Jake, Openshaw, Peter J M, Baillie, J Kenneth, Harrison, Ewen M, Docherty, Annemarie B, and Semple, Malcolm G

Published
2020

12. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol : prospective observational cohort study

Author

ISARIC4C investigators, Docherty, Annemarie B, Harrison, Ewen M, Green, Christopher A, Hardwick, Hayley E, Pius, Riinu, Norman, Lisa, Holden, Karl A, Read, Jonathan M, Dondelinger, Frank, Carson, Gail, Merson, Laura, Lee, James, Plotkin, Daniel, Sigfrid, Louise, Halpin, Sophie, Jackson, Clare, Gamble, Carrol, Horby, Peter W, Nguyen-Van-Tam, Jonathan S, Ho, Antonia, Russell, Clark D, Dunning, Jake, Openshaw, Peter JM, Baillie, J Kenneth, and Semple, Malcolm G

Published
2020

13. Adeno-associated virus 2 infection in children with non-A-E hepatitis

Author

Ho, Antonia, DIAMONDS consortium, ISARIC4C investigators, Willett, Brian J., Breuer, Judith, Semple, Malcolm G., Turner, David, Baillie, J. Kenneth, Thomson, Emma C., University of St Andrews. School of Medicine, University of St Andrews. St Andrews Bioinformatics Unit, University of St Andrews. Infection and Global Health Division, and University of St Andrews. Biomedical Sciences Research Complex

Subjects
SDG 3 - Good Health and Well-being, RA0421, RJ, RA0421 Public health. Hygiene. Preventive Medicine, MCP, RJ Pediatrics, DAS
Abstract

Funding: The work was funded by Public Health Scotland, the National Institute for Health Research (NIHR; award CO-CIN-01) and the Medical Research Council (MRC; grants MR/X010252/1, MC_UU_1201412, MC_UU_12018/12, MC_PC_19059, MC_PC_19025 & MC_PC_22004). DIAMONDS is funded by the European Union Horizon 2020 programme; grant 848196). MP acknowledges funding support from the Wellcome Trust (206369/Z/17/Z). MGS gratefully acknowledges funding support from The Pandemic Institute, Liverpool and the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, and United Kingdom Health Security Agency (United KingdomHSA). JKB gratefully acknowledges funding support from a Wellcome Trust Senior Research Fellowship (223164/Z/21/Z), and MC_PC_20029, Sepsis Research (Fiona Elizabeth Agnew Trust), a BBSRC Institute Strategic Programme Grant to the Roslin Institute (BB/P013732/1, BB/P013759/1), and the United Kingdom Intensive Care Society. An outbreak of acute hepatitis of unknown aetiology in children was reported in Scotland in April 20221 and has now been identified in 35 countries2. Several recent studies have suggested an association with human adenovirus (HAdV), a virus not commonly associated with hepatitis. Here we report a detailed case-control investigation and find an association between adeno-associated virus (AAV2) infection and host genetics in disease susceptibility. Using next-generation sequencing (NGS), reverse transcription-polymerase chain reaction (RT-PCR), serology and in situ hybridisation (ISH), we detected recent infection with AAV2 in the plasma and liver samples of 26/32 (81%) hepatitis cases versus 5/74 (7%) of controls. Further, AAV2 was detected within ballooned hepatocytes alongside a prominent T cell infiltrate in liver biopsies. In keeping with a CD4+ T-cell-mediated immune pathology, the Human Leucocyte Antigen (HLA) class II DRB1*04:01 allele was identified in 25/27 cases (93%), compared with a background frequency of 10/64 (16%; p=5.49 x 10-12). In summary, we report an outbreak of acute paediatric hepatitis associated with AAV2 infection (most likely acquired as a coinfection with HAdV which is required as a "helper virus" to support AAV2 replication) and HLA class II-related disease susceptibility. Postprint

Published
2023

14. Fewer COVID-19 neurological complications with dexamethasone and remdesivir

Author

Grundmann, A, Wu, C-H, Hardwick, M, Baillie, JK, Openshaw, PJ, Semple, MG, Böhning, D, Pett, S, Michael, BD, Thomas, RH, Galea, I, and ISARIC4C investigators

Subjects
Neurology, Neurology (clinical)
Abstract

OBJECTIVE: The objective of this study was to assess the impact of treatment with dexamethasone, remdesivir or both on neurological complications in acute coronavirus diease 2019 (COVID-19).METHODS: We used observational data from the International Severe Acute and emerging Respiratory Infection Consortium World Health Organization (WHO) Clinical Characterization Protocol, United Kingdom. Hospital inpatients aged ≥18 years with laboratory-confirmed severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection admitted between January 31, 2020, and June 29, 2021, were included. Treatment allocation was non-blinded and performed by reporting clinicians. A propensity scoring methodology was used to minimize confounding. Treatment with remdesivir, dexamethasone, or both was assessed against the standard of care. The primary outcome was a neurological complication occurring at the point of death, discharge, or resolution of the COVID-19 clinical episode.RESULTS: Out of 89,297 hospital inpatients, 64,088 had severe COVID-19 and 25,209 had non-hypoxic COVID-19. Neurological complications developed in 4.8% and 4.5%, respectively. In both groups, neurological complications were associated with increased mortality, intensive care unit (ICU) admission, worse self-care on discharge, and time to recovery. In patients with severe COVID-19, treatment with dexamethasone (n = 21,129), remdesivir (n = 1,428), and both combined (n = 10,846) were associated with a lower frequency of neurological complications: OR = 0.76 (95% confidence interval [CI] = 0.69-0.83), OR = 0.69 (95% CI = 0.51-0.90), and OR = 0.54 (95% CI = 0.47-0.61), respectively. In patients with non-hypoxic COVID-19, dexamethasone (n = 2,580) was associated with less neurological complications (OR = 0.78, 95% CI = 0.62-0.97), whereas the dexamethasone/remdesivir combination (n = 460) showed a similar trend (OR = 0.63, 95% CI = 0.31-1.15).INTERPRETATION: Treatment with dexamethasone, remdesivir, or both in patients hospitalized with COVID-19 was associated with a lower frequency of neurological complications in an additive manner, such that the greatest benefit was observed in patients who received both drugs together. ANN NEUROL 2022.

Published
2022
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15. The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

Author

Knight, Gwenan M, Pham, Thi Mui, Stimson, James, Funk, Sebastian, Jafari, Yalda, Pople, Diane, Evans, Stephanie, Yin, Mo, Brown, Colin S, Bhattacharya, Alex, Hope, Russell, Semple, Malcolm G, ISARIC4C Investigators, CMMID COVID-19 Working Group, Read, Jonathan M, Cooper, Ben S, and Robotham, Julie V

Abstract

BACKGROUND: SARS-CoV-2 is known to transmit in hospital settings, but the contribution of infections acquired in hospitals to the epidemic at a national scale is unknown. METHODS: We used comprehensive national English datasets to determine the number of COVID-19 patients with identified hospital-acquired infections (with symptom onset > 7 days after admission and before discharge) in acute English hospitals up to August 2020. As patients may leave the hospital prior to detection of infection or have rapid symptom onset, we combined measures of the length of stay and the incubation period distribution to estimate how many hospital-acquired infections may have been missed. We used simulations to estimate the total number (identified and unidentified) of symptomatic hospital-acquired infections, as well as infections due to onward community transmission from missed hospital-acquired infections, to 31st July 2020. RESULTS: In our dataset of hospitalised COVID-19 patients in acute English hospitals with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired. We estimated that only 30% (range across weeks and 200 simulations: 20-41%) of symptomatic hospital-acquired infections would be identified, with up to 15% (mean, 95% range over 200 simulations: 14.1-15.8%) of cases currently classified as community-acquired COVID-19 potentially linked to hospital transmission. We estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200-16,400) or 20.1% (19.2-20.7%) of all identified hospitalised COVID-19 cases. CONCLUSIONS: Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the "first wave" in England, but less than 1% of all infections in England. Using time to symptom onset from admission for inpatients as a detection method likely misses a substantial proportion (> 60%) of hospital-acquired infections.

Published
2022

16. Mapping of SARS-CoV-2 IgM and IgG in gingival crevicular fluid: antibody dynamics and linkage to severity of COVID-19 in hospital inpatients

Author

Ijaz, S, Dicks, S, Jegatheesan, K, Parker, E, Katsanovskaja, K, Vink, E, McClure, MO, Shute, J, Hope, J, Cook, N, Cherepanov, P, Turtle, L, Paxton, WA, Pollakis, G, Ho, A, Openshaw, PJM, Baillie, JK, Semple, MG, Tedder, RS, For ISARIC4C Investigators, National Institute for Health Research, UKRI MRC COVID-19 Rapid Response Call, and UK Research and Innovation

Subjects
Microbiology (medical), Inpatients, SARS-CoV-2, Genome Integrity & Repair, COVID-19, 1103 Clinical Sciences, Gingival Crevicular Fluid, for ISARIC4C Investigators, Biochemistry & Proteomics, Antibodies, Viral, Microbiology, Hospitals, Infectious Diseases, Immunoglobulin M, Immunoglobulin G, Cell Cycle & Chromosomes, Humans, Structural Biology & Biophysics
Abstract

The use of non-venous analytes has been a priority publichealth tool for the diagnosis, monitoring and surveillance of arange of pathogens. These methods, primarily based on DriedBlood Spots (DBS) and Gingival Crevicular Fluid (GCF), were firstdeveloped in the UK and have been applied to investigate outbreakand transmission events,1,2 to improve the diagnosis of infectionsin underserved populations3,4 in addition to monitoring infectiontrends and informing on the impact of interventions.5,6 The ability to use non-venous analytes for antigen, antibody and nucleic acid detection and characterisation has applications toanswer questions linked to SARS-CoV-2 infections. The virus has been shown to transmit efficiently between individuals and within communities,7–10 with the rapid spread of SARS-CoV-2 attributed to transmissions from asymptomatic but infected individuals.10–14The ease of sampling with non-venous analytes and the ability for self-collection allows for rapid and accessible individual and population-based diagnostics and monitoring of prevalence. The convenience and acceptability of sample collection permit population prevalence studies, for example in school children or in immunised self-isolating individual populations, providing an important mechanism for generating data for virus surveillance with a potential to inform policy. A key role of population antibody testing would be to characterise the relationship between the development and dynamics of antibody responses to infection, vaccination, and the impact of the measures on subsequent rates of transmission

Published
2022
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17. Comparison of UK paediatric SARS-CoV-2 admissions across the first and second pandemic waves

Author

Swann, Olivia V, Pollock, Louisa, Holden, Karl A, Munro, Alasdair P S, Bennett, Aisleen, Williams, Thomas C, Turtle, Lance, Fairfield, Cameron J, Drake, Thomas M, Faust, Saul N, Sinha, Ian P, Roland, Damian, Whittaker, Elizabeth, Ladhani, Shamez N, Nguyen-Van-Tam, Jonathan S, Girvan, Michelle, Donohue, Chloe, Donegan, Cara, Spencer, Rebecca G, Hardwick, Hayley E, Openshaw, Peter J M, Baillie, J Kenneth, Harrison, Ewen M, Docherty, Annemarie B, Semple, Malcolm G, and ISARIC4C Investigators

Subjects
Pediatrics, Perinatology and Child Health
Abstract

BACKGROUND: We hypothesised that the clinical characteristics of hospitalised children and young people (CYP) with SARS-CoV-2 in the UK second wave (W2) would differ from the first wave (W1) due to the alpha variant (B.1.1.7), school reopening and relaxation of shielding. METHODS: Prospective multicentre observational cohort study of patients

Published
2022

18. Vitamin D insufficiency in COVID-19 and influenza A, and critical illness survivors : a cross-sectional study

Author

Hurst, Emma A, Mellanby, Richard J, Handel, Ian, Griffith, David M, Rossi, Adriano G, Walsh, Timothy S, Shankar-Hari, Manu, Dunning, Jake, Homer, Natalie Z, Denham, Scott G, Devine, Kerri, Holloway, Paul A, Moore, Shona C, Thwaites, Ryan S, Samanta, Romit J, Summers, Charlotte, Hardwick, Hayley E, Oosthuyzen, Wilna, Turtle, Lance, Semple, Malcolm G, Openshaw, Peter JM, Baillie, J Kenneth, Russell, Clark D, ISARIC4C Investigators, Griffith, David M [0000-0001-9500-241X], Summers, Charlotte [0000-0002-7269-2873], Russell, Clark D [0000-0002-9873-8243], and Apollo - University of Cambridge Repository

Subjects
intensive & critical care, SARS-CoV-2, Critical Illness, COVID-19, Vitamin D Deficiency, immunology, respiratory infections, Cross-Sectional Studies, Influenza A Virus, H1N1 Subtype, Influenza, Human, Humans, Survivors, Vitamin D, Pandemics
Abstract

Objectives\ud \ud The steroid hormone vitamin D has roles in immunomodulation and bone health. Insufficiency is associated with susceptibility to respiratory infections. We report 25-hydroxy vitamin D (25(OH)D) measurements in hospitalised people with COVID-19 and influenza A and in survivors of critical illness to test the hypotheses that vitamin D insufficiency scales with illness severity and persists in survivors.\ud \ud \ud \ud Design\ud \ud Cross-sectional study.\ud \ud \ud \ud Setting and participants\ud \ud Plasma was obtained from 295 hospitalised people with COVID-19 (International Severe Acute Respiratory and emerging Infections Consortium (ISARIC)/WHO Clinical Characterization Protocol for Severe Emerging Infections UK study), 93 with influenza A (Mechanisms of Severe Acute Influenza Consortium (MOSAIC) study, during the 2009–2010 H1N1 pandemic) and 139 survivors of non-selected critical illness (prior to the COVID-19 pandemic). Total 25(OH)D was measured by liquid chromatography-tandem mass spectrometry. Free 25(OH)D was measured by ELISA in COVID-19 samples.\ud \ud \ud \ud Outcome measures\ud \ud Receipt of invasive mechanical ventilation (IMV) and in-hospital mortality.\ud \ud \ud \ud Results\ud \ud Vitamin D insufficiency (total 25(OH)D 25–50 nmol/L) and deficiency (

Published
2021

19. A prenylated dsRNA sensor protects against severe COVID-19

Author

Wickenhagen, A, Sugrue, E, Lytras, S, Kuchi, S, Noerenberg, M, Turnbull, ML, Loney, C, Herder, V, Allan, J, Jarmson, I, Cameron-Ruiz, N, Varjak, M, Pinto, RM, Lee, JY, Iselin, L, Palmalux, N, Stewart, DG, Swingler, S, Greenwood, EJD, Crozier, TWM, Gu, Q, Davies, EL, Clohisey, S, Wang, B, Trindade Maranhão Costa, F, Freire Santana, M, de Lima Ferreira, LC, Murphy, L, Fawkes, A, Meynert, A, Grimes, G, Da Silva Filho, JL, Marti, M, Hughes, J, Stanton, RJ, Wang, ECY, Ho, A, Davis, I, Jarrett, RF, Castello, A, Robertson, DL, Semple, MG, Openshaw, PJM, Palmarini, M, Lehner, PJ, Baillie, JK, Rihn, SJ, Wilson, SJ, ISARIC4C Investigators, Carson, G, Alex, B, Bach, B, Barclay, WS, Bogaert, D, Chand, M, Cooke, GS, Docherty, AB, Dunning, J, da Silva Filipe, A, Fletcher, T, Green, CA, Harrison, EM, Ho, AYW, Horby, PW, Ijaz, S, Khoo, S, Klenerman, P, Law, A, Lim, WS, Mentzer, AJ, Merson, L, Meynert, AM, Noursadeghi, M, Moore, SC, Paxton, WA, Pollakis, G, Price, N, Rambaut, A, Russell, CD, Sancho-Shimizu, V, Scott, JT, de Silva, T, Sigfrid, L, Solomon, T, Sriskandan, S, Stuart, D, Summers, C, Tedder, RS, Thomson, EC, Thompson, AAR, Thwaites, RS, Turtle, LCW, Gupta, RK, Palmieri, C, Swann, OV, Zambon, M, Dumas, M-E, Griffin, JL, Takats, Z, Chechi, K, Andrikopoulos, P, Osagie, A, Olanipekun, M, Liggi, S, Lewis, MR, Dos Santos Correia, G, Sands, CJ, Takis, P, Maslen, L, Hardwick, H, Donohue, C, Griffiths, F, Oosthuyzen, W, Donegan, C, Spencer, RG, Norman, L, Pius, R, Drake, TM, Fairfield, CJ, Knight, SR, Mclean, KA, Murphy, D, Shaw, CA, Dalton, J, Girvan, M, Saviciute, E, Roberts, S, Harrison, J, Marsh, L, Connor, M, Halpin, S, Jackson, C, Gamble, C, Plotkin, D, Lee, J, Leeming, G, Wham, M, Hendry, R, Scott-Brown, J, Greenhalf, W, Shaw, V, McDonald, SE, Keating, S, Ahmed, KA, Armstrong, JA, Ashworth, M, Asiimwe, IG, Bakshi, S, Barlow, SL, Booth, L, Brennan, B, Bullock, K, Catterall, BWA, Clark, JJ, Clarke, EA, Cole, S, Cooper, L, Cox, H, Davis, C, Dincarslan, O, Dunn, C, Dyer, P, Elliott, A, Evans, A, Finch, L, Fisher, LWS, Foster, T, Garcia-Dorival, I, Gunning, P, Hartley, C, Jensen, RL, Jones, CB, Jones, TR, Khandaker, S, King, K, Kiy, RT, Koukorava, C, Lake, A, Lant, S, Latawiec, D, Lavelle-Langham, L, Lefteri, D, Lett, L, Livoti, LA, Mancini, M, McDonald, S, McEvoy, L, McLauchlan, J, Metelmann, S, Miah, NS, Middleton, J, Mitchell, J, Murphy, EG, Penrice-Randal, R, Pilgrim, J, Prince, T, Reynolds, W, Ridley, PM, Sales, D, Shaw, VE, Shears, RK, Small, B, Subramaniam, KS, Szemiel, A, Taggart, A, Tanianis-Hughes, J, Thomas, J, Trochu, E, van Tonder, L, Wilcock, E, Zhang, JE, Flaherty, L, Maziere, N, Cass, E, Carracedo, AD, Carlucci, N, Holmes, A, Massey, H, Wrobel, N, McCafferty, S, Morrice, K, MacLean, A, Adeniji, K, Agranoff, D, Agwuh, K, Ail, D, Aldera, EL, Alegria, A, Allen, S, Angus, B, Ashish, A, Atkinson, D, Bari, S, Barlow, G, Barnass, S, Barrett, N, Bassford, C, Basude, S, Baxter, D, Beadsworth, M, Bernatoniene, J, Berridge, J, Berry, C, Best, N, Bothma, P, Chadwick, D, Brittain-Long, R, Bulteel, N, Burden, T, Burtenshaw, A, Caruth, V, Chambler, D, Chee, N, Child, J, Chukkambotla, S, Clark, T, Collini, P, Cosgrove, C, Cupitt, J, Cutino-Moguel, M-T, Dark, P, Dawson, C, Dervisevic, S, Donnison, P, Douthwaite, S, Drummond, A, DuRand, I, Dushianthan, A, Dyer, T, Evans, C, Eziefula, C, Fegan, C, Finn, A, Fullerton, D, Garg, S, Garg, A, Gkrania-Klotsas, E, Godden, J, Goldsmith, A, Graham, C, Hardy, E, Hartshorn, S, Harvey, D, Havalda, P, Hawcutt, DB, Hobrok, M, Hodgson, L, Hormis, A, Jacobs, M, Jain, S, Jennings, P, Kaliappan, A, Kasipandian, V, Kegg, S, Kelsey, M, Kendall, J, Kerrison, C, Kerslake, I, Koch, O, Koduri, G, Koshy, G, Laha, S, Laird, S, Larkin, S, Leiner, T, Lillie, P, Limb, J, Linnett, V, Little, J, Lyttle, M, MacMahon, M, MacNaughton, E, Mankregod, R, Masson, H, Matovu, E, McCullough, K, McEwen, R, Meda, M, Mills, G, Minton, J, Mirfenderesky, M, Mohandas, K, Mok, Q, Moon, J, Moore, E, Morgan, P, Morris, C, Mortimore, K, Moses, S, Mpenge, M, Mulla, R, Murphy, M, Nagel, M, Nagarajan, T, Nelson, M, Norris, L, O'Shea, MK, Otahal, I, Ostermann, M, Pais, M, Panchatsharam, S, Papakonstantinou, D, Paraiso, H, Patel, B, Pattison, N, Pepperell, J, Peters, M, Phull, M, Pintus, S, Singh Pooni, J, Planche, T, Post, F, Price, D, Prout, R, Rae, N, Reschreiter, H, Reynolds, T, Richardson, N, Roberts, M, Roberts, D, Rose, A, Rousseau, G, Ruge, B, Ryan, B, Saluja, T, Schmid, ML, Shah, A, Shanmuga, P, Sharma, A, Shawcross, A, Sizer, J, Shankar-Hari, M, Smith, R, Snelson, C, Spittle, N, Staines, N, Stambach, T, Stewart, R, Subudhi, P, Szakmany, T, Tatham, K, Thompson, C, Thompson, R, Tridente, A, Tupper-Carey, D, Twagira, M, Vallotton, N, Vancheeswaran, R, Vincent-Smith, L, Visuvanathan, S, Vuylsteke, A, Waddy, S, Wake, R, Walden, A, Welters, I, Whitehouse, T, Whittaker, P, Whittington, A, Papineni, P, Wijesinghe, M, Williams, M, Wilson, L, Winchester, S, Wiselka, M, Wolverson, A, Wootton, DG, Workman, A, Yates, B, Young, P, Division of Computational and Systems Medicine, Imperial College London, London, SW7 2AZ, UK, Imperial College London - National Heart and Lung Institute, Centre National de la Recherche Scientifique (CNRS), MRC - University of Glasgow Centre for Virus Research, University of Kent [Canterbury], University of Glasgow, National Institute for Health Research, UK Research and Innovation, UKRI MRC COVID-19 Rapid Response Call, Mentzer, AJ, Investigators, ISARIC4C, Lee, J, and Angus, B

Subjects
2',5'-Oligoadenylate Synthetase/genetics, L PATHWAY, viruses, SINGLE-NUCLEOTIDE POLYMORPHISM, RNASE-L, RNA, Viral/chemistry, RNA, Double-Stranded/chemistry, Virus Replication, Severity of Illness Index, COVID-19/enzymology, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Chiroptera, Sense (molecular biology), 2',5'-Oligoadenylate Synthetase, MESH: COVID-19, MESH: Animals, MESH: Interferons, ENZYME-ACTIVITY, MESH: 2',5'-Oligoadenylate Synthetase, Endoribonucleases/metabolism, 0303 health sciences, MESH: Protein Prenylation, Multidisciplinary, I INTERFERON, Coronaviridae/enzymology, MESH: Polymorphism, Single Nucleotide, 030302 biochemistry & molecular biology, MESH: Chiroptera, CORONAVIRUS REPLICATION, 3. Good health, Cell biology, Multidisciplinary Sciences, Isoenzymes, RNA silencing, VIRUS NS1 PROTEIN, MESH: RNA, Viral, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, MESH: Isoenzymes, Science & Technology - Other Topics, RNA, Viral, MESH: 5' Untranslated Regions, [SDV.IMM]Life Sciences [q-bio]/Immunology, SARS-CORONAVIRUS, Gene isoform, MESH: Endoribonucleases, Retroelements, RNase P, Coronaviridae, General Science & Technology, Protein Prenylation, Biology, Polymorphism, Single Nucleotide, Chiroptera/genetics, 03 medical and health sciences, MESH: RNA, Double-Stranded, MESH: Retroelements, SARS-CoV-2/genetics, MESH: Severity of Illness Index, Endoribonucleases, Animals, Humans, MESH: SARS-CoV-2, Interferons/immunology, Gene, ISARIC4C Investigators, 030304 developmental biology, RNA, Double-Stranded, Science & Technology, MESH: Humans, ACCEPTOR SITE, Phosphoric Diester Hydrolases, SARS-CoV-2, fungi, MESH: Virus Replication, RNA, COVID-19, Phosphoric Diester Hydrolases/genetics, Viral replication, A549 Cells, Protein prenylation, SPLICE-SITE POLYMORPHISM, Interferons, MESH: A549 Cells, 5' Untranslated Regions, MESH: Phosphoric Diester Hydrolases, Isoenzymes/genetics, MESH: Coronaviridae
Abstract

International audience; INTRODUCTIONInterferons (IFNs) are cytokines that are rapidly deployed in response to invading pathogens. By initiating a signaling cascade that stimulates the expression of hundreds of genes, IFNs create an antiviral state in host cells. Because IFNs heavily influence COVID-19 outcomes, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication can be inhibited by the antiviral state, it is important to understand how the individual antiviral effectors encoded by IFN-stimulated genes (ISGs) inhibit SARS-CoV-2.RATIONALEWe hypothesized that IFN-stimulated antiviral effectors can inhibit SARS-CoV-2, and that variation at the loci encoding these defenses underlies why some people are more susceptible to severe COVID-19.RESULTSWe used arrayed ISG expression screening to reveal that 2′-5′-oligoadenylate synthetase 1 (OAS1) consistently inhibited SARS-CoV-2 in different contexts. Using CRISPR-Cas9, we found that endogenous OAS1 makes a substantial contribution to the antiviral state by recognizing short stretches of double-stranded RNA (dsRNA) and activating RNase L. We globally mapped where OAS1 binds to SARS-CoV-2 viral RNAs and found that OAS1 binding is remarkably specific, with two conserved stem loops in the SARS-CoV-2 5′-untranslated region (UTR) constituting the principal viral target.OAS1 expression was readily detectable at the sites of infection in individuals who died of COVID-19, and specific OAS1 alleles are known to be associated with altered susceptibility to infection and severe disease. It had previously been reported that alleles containing a common splice-acceptor single nucleotide polymorphism in OAS1 (Rs10774671) were associated with less severe COVID-19. We determined that people with at least one allele with a G at this position could express a prenylated form of OAS1 (p46), whereas other individuals could not. Using a series of mutants, we found that C-terminal prenylation was necessary for OAS1 to initiate a block to SARS-CoV-2. Furthermore, confocal microscopy revealed that prenylation targeted OAS1 to perinuclear structures rich in viral dsRNA, whereas non-prenylated OAS1 was diffusely localized and unable to initiate a detectable block to SARS-CoV-2 replication.The realization that prenylation is essential for OAS1-mediated sensing of SARS-CoV-2 allowed us to examine the transcriptome of infected patients and investigate whether there was a link between the expression of prenylated OAS1 and SARS-CoV-2 disease progression. Analysis of the OAS1 transcripts from 499 hospitalized COVID-19 patients revealed that expressing prenylated OAS1 was associated with protection from severe COVID-19.Because prenylated OAS1 was so important in human cases, we wanted to determine whether horseshoe bats, the likely source of SARS-CoV-2, possessed the same defense. When we examined the genomic region where the prenylation signal should reside, retrotransposition of a long terminal repeat sequence had ablated this signal, preventing the expression of prenylated anti-CoV OAS1 in these bats.CONCLUSIONC-terminal prenylation targets OAS1 to intracellular sites rich in viral dsRNA, which are likely the SARS-CoV-2 replicative organelles. Once in the right place, OAS1 binds to dsRNA structures in the SARS-CoV-2 5′-UTR and initiates a potent block to SARS-CoV-2 replication. Thus, the correct targeting of OAS1 and the subsequent inhibition of SARS-CoV-2 likely underpins the genetic association of alleles containing a G at Rs10774671 with reduced susceptibility to infection and severe disease in COVID-19. Moreover, the conspicuous absence of this antiviral defense in horseshoe bats potentially explains why SARS-CoV-2 is so sensitive to this defense in humans.

Published
2021
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20. Changes in in-hospital mortality in the first wave of COVID-19: a multicentre prospective observational cohort study using the WHO Clinical Characterisation Protocol UK

Author

Docherty, Annemarie B, Mulholland, Rachel H, Lone, Nazir I, Cheyne, Christopher P, De Angelis, Daniela, Diaz-Ordaz, Karla, Donegan, Cara, Drake, Thomas M, Dunning, Jake, Funk, Sebastian, García-Fiñana, Marta, Girvan, Michelle, Hardwick, Hayley E, Harrison, Janet, Ho, Antonia, Hughes, David M, Keogh, Ruth H, Kirwan, Peter D, Leeming, Gary, Nguyen Van-Tam, Jonathan S, Pius, Riinu, Russell, Clark D, Spencer, Rebecca G, Tom, Brian Dm, Turtle, Lance, Openshaw, Peter Jm, Baillie, J Kenneth, Harrison, Ewen M, Semple, Malcolm G, and ISARIC4C Investigators

Abstract

BACKGROUND: Mortality rates in hospitalised patients with COVID-19 in the UK appeared to decline during the first wave of the pandemic. We aimed to quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital. METHODS: In this multicentre prospective observational cohort study, the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK recruited a prospective cohort of patients with COVID-19 admitted to 247 acute hospitals in England, Scotland, and Wales during the first wave of the pandemic (between March 9 and Aug 2, 2020). We included all patients aged 18 years and older with clinical signs and symptoms of COVID-19 or confirmed COVID-19 (by RT-PCR test) from assumed community-acquired infection. We did a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and in-hospital mortality, adjusting for confounders (demographics, comorbidities, and severity of illness) and quantifying potential mediators (level of respiratory support and steroid treatment). The primary outcome was weekly in-hospital mortality at 28 days, defined as the proportion of patients who had died within 28 days of admission of all patients admitted in the observed week, and it was assessed in all patients with an outcome. This study is registered with the ISRCTN Registry, ISRCTN66726260. FINDINGS: Between March 9, and Aug 2, 2020, we recruited 80 713 patients, of whom 63 972 were eligible and included in the study. Unadjusted weekly in-hospital mortality declined from 32·3% (95% CI 31·8-32·7) in March 9 to April 26, 2020, to 16·4% (15·0-17·8) in June 15 to Aug 2, 2020. Reductions in mortality were observed in all age groups, in all ethnic groups, for both sexes, and in patients with and without comorbidities. After adjustment, there was a 32% reduction in the risk of mortality per 7-week period (odds ratio [OR] 0·68 [95% CI 0·65-0·71]). The higher proportions of patients with severe disease and comorbidities earlier in the first wave (March and April) than in June and July accounted for 10·2% of this reduction. The use of respiratory support changed during the first wave, with gradually increased use of non-invasive ventilation over the first wave. Changes in respiratory support and use of steroids accounted for 22·2%, OR 0·95 (0·94-0·95) of the reduction in in-hospital mortality. INTERPRETATION: The reduction in in-hospital mortality in patients with COVID-19 during the first wave in the UK was partly accounted for by changes in the case-mix and illness severity. A significant reduction in in-hospital mortality was associated with differences in respiratory support and critical care use, which could partly reflect accrual of clinical knowledge. The remaining improvement in in-hospital mortality is not explained by these factors, and could be associated with changes in community behaviour, inoculum dose, and hospital capacity strain. FUNDING: National Institute for Health Research and the Medical Research Council.

Published
2021

21. Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

Author

Leclerc, Quentin J, Fuller, Naomi M, Keogh, Ruth H, Diaz-Ordaz, Karla, Sekula, Richard, Semple, Malcolm G, ISARIC4C Investigators, CMMID COVID-19 Working Group, Atkins, Katherine E, Procter, Simon R, and Knight, Gwenan M

Abstract

BACKGROUND: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. METHODS: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. RESULTS: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. CONCLUSIONS: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. TRIAL REGISTRATION: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

Published
2021

22. Association of tiered restrictions and a second lockdown with COVID-19 deaths and hospital admissions in England: a modelling study

Author

Davies, Nicholas G, Barnard, Rosanna C, Jarvis, Christopher I, Russell, Timothy W, Semple, Malcolm G, Jit, Mark, Edmunds, W John, Centre for Mathematical Modelling of Infectious Diseases COVID-1, and ISARIC4C investigators

Abstract

BACKGROUND: A second wave of COVID-19 cases in autumn, 2020, in England led to localised, tiered restrictions (so-called alert levels) and, subsequently, a second national lockdown. We examined the impact of these tiered restrictions, and alternatives for lockdown stringency, timing, and duration, on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and hospital admissions and deaths from COVID-19. METHODS: We fit an age-structured mathematical model of SARS-CoV-2 transmission to data on hospital admissions and hospital bed occupancy (ISARIC4C/COVID-19 Clinical Information Network, National Health Service [NHS] England), seroprevalence (Office for National Statistics, UK Biobank, REACT-2 study), virology (REACT-1 study), and deaths (Public Health England) across the seven NHS England regions from March 1, to Oct 13, 2020. We analysed mobility (Google Community Mobility) and social contact (CoMix study) data to estimate the effect of tiered restrictions implemented in England, and of lockdowns implemented in Northern Ireland and Wales, in October, 2020, and projected epidemiological scenarios for England up to March 31, 2021. FINDINGS: We estimated a reduction in the effective reproduction number (Rt) of 2% (95% credible interval [CrI] 0-4) for tier 2, 10% (6-14) for tier 3, 35% (30-41) for a Northern Ireland-stringency lockdown with schools closed, and 44% (37-49) for a Wales-stringency lockdown with schools closed. From Oct 1, 2020, to March 31, 2021, a projected COVID-19 epidemic without tiered restrictions or lockdown results in 280 000 (95% projection interval 274 000-287 000) hospital admissions and 58 500 (55 800-61 100) deaths. Tiered restrictions would reduce hospital admissions to 238 000 (231 000-245 000) and deaths to 48 600 (46 400-50 700). From Nov 5, 2020, a 4-week Wales-type lockdown with schools remaining open-similar to the lockdown measures announced in England in November, 2020-was projected to further reduce hospital admissions to 186 000 (179 000-193 000) and deaths to 36 800 (34 900-38 800). Closing schools was projected to further reduce hospital admissions to 157 000 (152 000-163 000) and deaths to 30 300 (29 000-31 900). A projected lockdown of greater than 4 weeks would reduce deaths but would bring diminishing returns in reducing peak pressure on hospital services. An earlier lockdown would have reduced deaths and hospitalisations in the short term, but would lead to a faster resurgence in cases after January, 2021. In a post-hoc analysis, we estimated that the second lockdown in England (Nov 5-Dec 2) reduced Rt by 22% (95% CrI 15-29), rather than the 32% (25-39) reduction estimated for a Wales-stringency lockdown with schools open. INTERPRETATION: Lockdown measures outperform less stringent restrictions in reducing cumulative deaths. We projected that the lockdown policy announced to commence in England on Nov 5, with a similar stringency to the lockdown adopted in Wales, would reduce pressure on the health service and would be well timed to suppress deaths over the winter period, while allowing schools to remain open. Following completion of the analysis, we analysed new data from November, 2020, and found that despite similarities in policy, the second lockdown in England had a smaller impact on behaviour than did the second lockdown in Wales, resulting in more deaths and hospitalisations than we originally projected when focusing on a Wales-stringency scenario for the lockdown. FUNDING: Horizon 2020, UK Medical Research Council, and the National Institute for Health Research.

Published
2020

23. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study

Author

Docherty, Annemarie B., Harrison, Ewen M., Green, Christopher, Hardwick, Hayley E., Pius, Riinu, Norman, Lisa, Holden, Karl A., Read, Jonathan M., Dondelinger, Frank, Carson, Gail, Merson, Laura, Lee, James, Plotkin, Daniel, Sigfrid, Louise, Halpin, Sophie, Jackson, Clare, Gamble, Carrol, Horby, Peter W., Nguyen-Van-Tam, Jonathan S., Ho, Antonia, Russell, Clark D., Dunning, Jake, Openshaw, Peter J.M., Baillie, J. Kenneth, Semple, Malcolm, and on behalf of the ISARIC4C investigators, .

Abstract

Objective: To characterise the clinical features of patients admitted to hospital with coronavirus disease 2019 (covid-19) in the United Kingdom during the growth phase of the first wave of this outbreak who were enrolled in the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study, and to explore risk factors associated with mortality in hospital. Design: Prospective observational cohort study with rapid data gathering and near real time analysis. Setting: 208 acute care hospitals in England, Wales, and Scotland between 6 February and 19 April 2020. A case report form developed by ISARIC and WHO was used to collect clinical data. A minimal follow-up time of two weeks (to 3 May 2020) allowed most patients to complete their hospital admission. Participants: 20 133 hospital inpatients with covid-19. Main outcome measures: Admission to critical care (high dependency unit or intensive care unit) and mortality in hospital. Results: The median age of patients admitted to hospital with covid-19, or with a diagnosis of covid-19 made in hospital, was 73 years (interquartile range 58-82, range 0-104). More men were admitted than women (men 60%, n=12 068; women 40%, n=8065). The median duration of symptoms before admission was 4 days (interquartile range 1-8). The commonest comorbidities were chronic cardiac disease (31%, 5469/17 702), uncomplicated diabetes (21%, 3650/17 599), non-asthmatic chronic pulmonary disease (18%, 3128/17 634), and chronic kidney disease (16%, 2830/17 506); 23% (4161/18 525) had no reported major comorbidity. Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the reporting date. 17% (3001/18 183) required admission to high dependency or intensive care units; of these, 28% (826/3001) were discharged alive, 32% (958/3001) died, and 41% (1217/3001) continued to receive care at the reporting date. Of those receiving mechanical ventilation, 17% (276/1658) were discharged alive, 37% (618/1658) died, and 46% (764/1658) remained in hospital. Increasing age, male sex, and comorbidities including chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, liver disease and obesity were associated with higher mortality in hospital. Conclusions: ISARIC WHO CCP-UK is a large prospective cohort study of patients in hospital with covid-19. The study continues to enrol at the time of this report. In study participants, mortality was high, independent risk factors were increasing age, male sex, and chronic comorbidity, including obesity. This study has shown the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks. Study registration: ISRCTN66726260.

Published
2020

24. Obesity, Ethnicity, and Risk of Critical Care, Mechanical Ventilation, and Mortality in Patients Admitted to Hospital with COVID-19: Analysis of the ISARIC CCP-UK Cohort.

Author

Yates, Thomas, Zaccardi, Francesco, Islam, Nazrul, Razieh, Cameron, Gillies, Clare L., Lawson, Claire A., Chudasama, Yogini, Rowlands, Alex, Davies, Melanie J., Docherty, Annemarie B., Openshaw, Peter J. M., Baillie, J. Kenneth, Semple, Malcolm G., Khunti, Kamlesh, Openshaw, Peter Jm, and ISARIC4C investigators

Subjects
HOSPITAL mortality, COVID-19, ARTIFICIAL respiration, CRITICAL care medicine, ETHNICITY
Abstract

Objective: The aim of this study was to investigate the association of obesity with in-hospital coronavirus disease 2019 (COVID-19) outcomes in different ethnic groups.Methods: Patients admitted to hospital with COVID-19 in the United Kingdom through the Clinical Characterisation Protocol UK (CCP-UK) developed by the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) were included from February 6 to October 12, 2020. Ethnicity was classified as White, South Asian, Black, and other minority ethnic groups. Outcomes were admission to critical care, mechanical ventilation, and in-hospital mortality, adjusted for age, sex, and chronic diseases.Results: Of the participants included, 54,254 (age = 76 years; 45.0% women) were White, 3,728 (57 years; 41.1% women) were South Asian, 2,523 (58 years; 44.9% women) were Black, and 5,427 (61 years; 40.8% women) were other ethnicities. Obesity was associated with all outcomes in all ethnic groups, with associations strongest for black ethnicities. When stratified by ethnicity and obesity status, the odds ratios for admission to critical care, mechanical ventilation, and mortality in black ethnicities with obesity were 3.91 (3.13-4.88), 5.03 (3.94-6.63), and 1.93 (1.49-2.51), respectively, compared with White ethnicities without obesity.Conclusions: Obesity was associated with an elevated risk of in-hospital COVID-19 outcomes in all ethnic groups, with associations strongest in Black ethnicities. [ABSTRACT FROM AUTHOR]

Published
2021
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25. Fatal COVID-19 outcomes are associated with an antibody response targeting epitopes shared with endemic coronaviruses

Author

Anna L. McNaughton, Robert S. Paton, Matthew Edmans, Jonathan Youngs, Judith Wellens, Prabhjeet Phalora, Alex Fyfe, Sandra Belij-Rammerstorfer, Jai S. Bolton, Jonathan Ball, George W. Carnell, Wanwisa Dejnirattisai, Christina Dold, David W. Eyre, Philip Hopkins, Alison Howarth, Kreepa Kooblall, Hannah Klim, Susannah Leaver, Lian Ni Lee, César López-Camacho, Sheila F. Lumley, Derek C. Macallan, Alexander J. Mentzer, Nicholas M. Provine, Jeremy Ratcliff, Jose Slon-Compos, Donal Skelly, Lucas Stolle, Piyada Supasa, Nigel Temperton, Chris Walker, Beibei Wang, Duncan Wyncoll, Oxford Protective T Cell Immunology for COVID-19 (OPTIC) consortium, Scottish National Blood Transfusion Service (SNBTS) consortium, Peter Simmonds, Teresa Lambe, John Kenneth Baillie, Malcolm G. Semple, Peter J.M. Openshaw, International Severe Acute Respiratory and emerging Infection Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) investigators, Uri Obolski, Marc Turner, Miles Carroll, Juthathip Mongkolsapaya, Gavin Screaton, Stephen H. Kennedy, Lisa Jarvis, Eleanor Barnes, Susanna Dunachie, José Lourenço, Philippa C. Matthews, Tihana Bicanic, Paul Klenerman, Sunetra Gupta, and Craig P. Thompson

Subjects
Immunology, Infectious disease, Medicine
Abstract

The role of immune responses to previously seen endemic coronavirus epitopes in severe acute respiratory coronavirus 2 (SARS-CoV-2) infection and disease progression has not yet been determined. Here, we show that a key characteristic of fatal outcomes with coronavirus disease 2019 (COVID-19) is that the immune response to the SARS-CoV-2 spike protein is enriched for antibodies directed against epitopes shared with endemic beta-coronaviruses and has a lower proportion of antibodies targeting the more protective variable regions of the spike. The magnitude of antibody responses to the SARS-CoV-2 full-length spike protein, its domains and subunits, and the SARS-CoV-2 nucleocapsid also correlated strongly with responses to the endemic beta-coronavirus spike proteins in individuals admitted to an intensive care unit (ICU) with fatal COVID-19 outcomes, but not in individuals with nonfatal outcomes. This correlation was found to be due to the antibody response directed at the S2 subunit of the SARS-CoV-2 spike protein, which has the highest degree of conservation between the beta-coronavirus spike proteins. Intriguingly, antibody responses to the less cross-reactive SARS-CoV-2 nucleocapsid were not significantly different in individuals who were admitted to an ICU with fatal and nonfatal outcomes, suggesting an antibody profile in individuals with fatal outcomes consistent with an “original antigenic sin” type response.

Published
2022
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26. Association of tiered restrictions and a second lockdown with COVID-19 deaths and hospital admissions in England: a modelling study.

Author

Davies, Nicholas G, Barnard, Rosanna C, Jarvis, Christopher I, Russell, Timothy W, Semple, Malcolm G, Jit, Mark, Edmunds, W John, Centre for Mathematical Modelling of Infectious Diseases COVID-19 Working Group, and ISARIC4C investigators

Subjects
*STAY-at-home orders, *COVID-19, *HOSPITAL admission & discharge, *COVID-19 pandemic
Abstract

Background: A second wave of COVID-19 cases in autumn, 2020, in England led to localised, tiered restrictions (so-called alert levels) and, subsequently, a second national lockdown. We examined the impact of these tiered restrictions, and alternatives for lockdown stringency, timing, and duration, on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and hospital admissions and deaths from COVID-19.Methods: We fit an age-structured mathematical model of SARS-CoV-2 transmission to data on hospital admissions and hospital bed occupancy (ISARIC4C/COVID-19 Clinical Information Network, National Health Service [NHS] England), seroprevalence (Office for National Statistics, UK Biobank, REACT-2 study), virology (REACT-1 study), and deaths (Public Health England) across the seven NHS England regions from March 1, to Oct 13, 2020. We analysed mobility (Google Community Mobility) and social contact (CoMix study) data to estimate the effect of tiered restrictions implemented in England, and of lockdowns implemented in Northern Ireland and Wales, in October, 2020, and projected epidemiological scenarios for England up to March 31, 2021.Findings: We estimated a reduction in the effective reproduction number (Rt) of 2% (95% credible interval [CrI] 0-4) for tier 2, 10% (6-14) for tier 3, 35% (30-41) for a Northern Ireland-stringency lockdown with schools closed, and 44% (37-49) for a Wales-stringency lockdown with schools closed. From Oct 1, 2020, to March 31, 2021, a projected COVID-19 epidemic without tiered restrictions or lockdown results in 280 000 (95% projection interval 274 000-287 000) hospital admissions and 58 500 (55 800-61 100) deaths. Tiered restrictions would reduce hospital admissions to 238 000 (231 000-245 000) and deaths to 48 600 (46 400-50 700). From Nov 5, 2020, a 4-week Wales-type lockdown with schools remaining open-similar to the lockdown measures announced in England in November, 2020-was projected to further reduce hospital admissions to 186 000 (179 000-193 000) and deaths to 36 800 (34 900-38 800). Closing schools was projected to further reduce hospital admissions to 157 000 (152 000-163 000) and deaths to 30 300 (29 000-31 900). A projected lockdown of greater than 4 weeks would reduce deaths but would bring diminishing returns in reducing peak pressure on hospital services. An earlier lockdown would have reduced deaths and hospitalisations in the short term, but would lead to a faster resurgence in cases after January, 2021. In a post-hoc analysis, we estimated that the second lockdown in England (Nov 5-Dec 2) reduced Rt by 22% (95% CrI 15-29), rather than the 32% (25-39) reduction estimated for a Wales-stringency lockdown with schools open.Interpretation: Lockdown measures outperform less stringent restrictions in reducing cumulative deaths. We projected that the lockdown policy announced to commence in England on Nov 5, with a similar stringency to the lockdown adopted in Wales, would reduce pressure on the health service and would be well timed to suppress deaths over the winter period, while allowing schools to remain open. Following completion of the analysis, we analysed new data from November, 2020, and found that despite similarities in policy, the second lockdown in England had a smaller impact on behaviour than did the second lockdown in Wales, resulting in more deaths and hospitalisations than we originally projected when focusing on a Wales-stringency scenario for the lockdown.Funding: Horizon 2020, UK Medical Research Council, and the National Institute for Health Research. [ABSTRACT FROM AUTHOR]

Published
2021
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27. SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination

Author

Felicity Liew, Shubha Talwar, Andy Cross, Brian J. Willett, Sam Scott, Nicola Logan, Matthew K. Siggins, Dawid Swieboda, Jasmin K. Sidhu, Claudia Efstathiou, Shona C. Moore, Chris Davis, Noura Mohamed, Jose Nunag, Clara King, A.A. Roger Thompson, Sarah L. Rowland-Jones, Annemarie B. Docherty, James D. Chalmers, Ling-Pei Ho, Alexander Horsley, Betty Raman, Krisnah Poinasamy, Michael Marks, Onn Min Kon, Luke Howard, Daniel G. Wootton, Susanna Dunachie, Jennifer K. Quint, Rachael A. Evans, Louise V. Wain, Sara Fontanella, Thushan I. de Silva, Antonia Ho, Ewen Harrison, J. Kenneth Baillie, Malcolm G. Semple, Christopher Brightling, Ryan S. Thwaites, Lance Turtle, Peter J.M. Openshaw, Beatrice Alex, Petros Andrikopoulos, Benjamin Bach, Wendy S. Barclay, Debby Bogaert, Meera Chand, Kanta Chechi, Graham S. Cooke, Ana da Silva Filipe, Thushan de Silva, Gonçalo dos Santos Correia, Marc-Emmanuel Dumas, Jake Dunning, Tom Fletcher, Christopher A. Green, William Greenhalf, Julian Griffin, Rishi K. Gupta, Ewen M. Harrison, Antonia Y.W. Ho, Karl Holden, Peter W. Horby, Samreen Ijaz, Say Khoo, Paul Klenerman, Andrew Law, Matthew Lewis, Sonia Liggi, Wei Shen Lim, Lynn Maslen, Alexander J. Mentzer, Laura Merson, Alison M Meynert, Mahdad Noursadeghi, Michael Olanipekun, Anthonia Osagie, Massimo Palmarini, Carlo Palmieri, William A. Paxton, Georgios Pollakis, Nicholas Price, Andrew Rambaut, David L Robertson, Clark D. Russell, Vanessa Sancho-Shimizu, Caroline Sands, Janet T. Scott, Louise Sigfrid, Tom Solomon, Shiranee Sriskandan, David Stuart, Charlotte Summers, Olivia V. Swann, Zoltan Takats, Panteleimon Takis, Richard S. Tedder, Emma C. Thomson, Lance C.W. Turtle, Maria Zambon, Thomas M. Drake, Cameron J. Fairfield, Stephen R. Knight, Kenneth A. Mclean, Derek Murphy, Lisa Norman, Riinu Pius, Catherine A. Shaw, Marie Connor, Jo Dalton, Carrol Gamble, Michelle Girvan, Sophie Halpin, Janet Harrison, Clare Jackson, James Lee, Laura Marsh, Daniel Plotkin, Stephanie Roberts, Egle Saviciute, Sara Clohisey, Ross Hendry, Susan Knight, Eva Lahnsteiner, Gary Leeming, Lucy Norris, James Scott-Brown, Sarah Tait, Murray Wham, Richard Clark, Audrey Coutts, Lorna Donelly, Angie Fawkes, Tammy Gilchrist, Katarzyna Hafezi, Louise MacGillivray, Alan Maclean, Sarah McCafferty, Kirstie Morrice, Lee Murphy, Nicola Wrobel, Gail Carson, Kayode Adeniji, Daniel Agranoff, Ken Agwuh, Dhiraj Ail, Erin L. Aldera, Ana Alegria, Sam Allen, Brian Angus, Abdul Ashish, Dougal Atkinson, Shahedal Bari, Gavin Barlow, Stella Barnass, Nicholas Barrett, Christopher Bassford, Sneha Basude, David Baxter, Michael Beadsworth, Jolanta Bernatoniene, John Berridge, Colin Berry, Nicola Best, Pieter Bothma, Robin Brittain-Long, Naomi Bulteel, Tom Burden, Andrew Burtenshaw, Vikki Caruth, David Chadwick, Duncan Chambler, Nigel Chee, Jenny Child, Srikanth Chukkambotla, Tom Clark, Paul Collini, Catherine Cosgrove, Jason Cupitt, Maria-Teresa Cutino-Moguel, Paul Dark, Chris Dawson, Samir Dervisevic, Phil Donnison, Sam Douthwaite, Andrew Drummond, Ingrid DuRand, Ahilanadan Dushianthan, Tristan Dyer, Cariad Evans, Chi Eziefula, Chrisopher Fegan, Adam Finn, Duncan Fullerton, Sanjeev Garg, Atul Garg, Effrossyni Gkrania-Klotsas, Jo Godden, Arthur Goldsmith, Clive Graham, Tassos Grammatikopoulos, Elaine Hardy, Stuart Hartshorn, Daniel Harvey, Peter Havalda, Daniel B. Hawcutt, Maria Hobrok, Luke Hodgson, Anil Hormis, Joanne Howard, Michael Jacobs, Susan Jain, Paul Jennings, Agilan Kaliappan, Vidya Kasipandian, Stephen Kegg, Michael Kelsey, Jason Kendall, Caroline Kerrison, Ian Kerslake, Oliver Koch, Gouri Koduri, George Koshy, Shondipon Laha, Steven Laird, Susan Larkin, Tamas Leiner, Patrick Lillie, James Limb, Vanessa Linnett, Jeff Little, Mark Lyttle, Michael MacMahon, Emily MacNaughton, Ravish Mankregod, Huw Masson, Elijah Matovu, Katherine McCullough, Ruth McEwen, Manjula Meda, Gary Mills, Jane Minton, Kavya Mohandas, Quen Mok, James Moon, Elinoor Moore, Patrick Morgan, Craig Morris, Katherine Mortimore, Samuel Moses, Mbiye Mpenge, Rohinton Mulla, Michael Murphy, Thapas Nagarajan, Megan Nagel, Mark Nelson, Lillian Norris, Matthew K. O'Shea, Marlies Ostermann, Igor Otahal, Mark Pais, Selva Panchatsharam, Danai Papakonstantinou, Padmasayee Papineni, Hassan Paraiso, Brij Patel, Natalie Pattison, Justin Pepperell, Mark Peters, Mandeep Phull, Stefania Pintus, Tim Planche, Frank Post, David Price, Rachel Prout, Nikolas Rae, Henrik Reschreiter, Tim Reynolds, Neil Richardson, Mark Roberts, Devender Roberts, Alistair Rose, Guy Rousseau, Bobby Ruge, Brendan Ryan, Taranprit Saluja, Sarah Sarah, Matthias Schmid, Aarti Shah, Manu Shankar-Hari, Prad Shanmuga, Anil Sharma, Anna Shawcross, Jagtur Singh Pooni, Jeremy Sizer, Richard Smith, Catherine Snelson, Nick Spittle, Nikki Staines, Tom Stambach, Richard Stewart, Pradeep Subudhi, Tamas Szakmany, Kate Tatham, Jo Thomas, Chris Thompson, Robert Thompson, Ascanio Tridente, Darell Tupper-Carey, Mary Twagira, Nick Vallotton, Rama Vancheeswaran, Rachel Vincent, Lisa Vincent-Smith, Shico Visuvanathan, Alan Vuylsteke, Sam Waddy, Rachel Wake, Andrew Walden, Ingeborg Welters, Tony Whitehouse, Paul Whittaker, Ashley Whittington, Meme Wijesinghe, Martin Williams, Lawrence Wilson, Stephen Winchester, Martin Wiselka, Adam Wolverson, Daniel G Wootton, Andrew Workman, Bryan Yates, Peter Young, Sarah E. McDonald, Victoria Shaw, Katie A. Ahmed, Jane A. Armstrong, Milton Ashworth, Innocent G. Asiimwe, Siddharth Bakshi, Samantha L Barlow, Laura Booth, Benjamin Brennan, Katie Bullock, Nicola Carlucci, Emily Cass, Benjamin W.A. Catterall, Jordan J. Clark, Emily A. Clarke, Sarah Cole, Louise Cooper, Helen Cox, Christopher Davis, Oslem Dincarslan, Alejandra Doce Carracedo, Chris Dunn, Philip Dyer, Angela Elliott, Anthony Evans, Lorna Finch, Lewis W.S. Fisher, Lisa Flaherty, Terry Foster, Isabel Garcia-Dorival, Philip Gunning, Catherine Hartley, Anthony Holmes, Rebecca L. Jensen, Christopher B. Jones, Trevor R. Jones, Shadia Khandaker, Katharine King, Robyn T. Kiy, Chrysa Koukorava, Annette Lake, Suzannah Lant, Diane Latawiec, Lara Lavelle-Langham, Daniella Lefteri, Lauren Lett, Lucia A Livoti, Maria Mancini, Hannah Massey, Nicole Maziere, Sarah McDonald, Laurence McEvoy, John McLauchlan, Soeren Metelmann, Nahida S. Miah, Joanna Middleton, Joyce Mitchell, Ellen G Murphy, Rebekah Penrice-Randal, Jack Pilgrim, Tessa Prince, Will Reynolds, P. Matthew Ridley, Debby Sales, Victoria E. Shaw, Rebecca K. Shears, Benjamin Small, Krishanthi S. Subramaniam, Agnieska Szemiel, Aislynn Taggart, Jolanta Tanianis-Hughes, Jordan Thomas, Erwan Trochu, Libby van Tonder, Eve Wilcock, J. Eunice Zhang, Seán Keating, Cara Donegan, Rebecca G. Spencer, Chloe Donohue, Fiona Griffiths, Hayley Hardwick, Wilna Oosthuyzen, K. Abel, H. Adamali, D. Adeloye, O. Adeyemi, R. Adrego, L.A. Aguilar Jimenez, S. Ahmad, N. Ahmad Haider, R. Ahmed, N. Ahwireng, M. Ainsworth, B. Al-Sheklly, A. Alamoudi, M. Ali, M. Aljaroof, A.M. All, L. Allan, R.J. Allen, L. Allerton, L. Allsop, P. Almeida, D. Altmann, M. Alvarez Corral, S. Amoils, D. Anderson, C. Antoniades, G. Arbane, A. Arias, C. Armour, L. Armstrong, N. Armstrong, D. Arnold, H. Arnold, A. Ashish, A. Ashworth, M. Ashworth, S. Aslani, H. Assefa-Kebede, C. Atkin, P. Atkin, R. Aul, H. Aung, L. Austin, C. Avram, A. Ayoub, M. Babores, R. Baggott, J. Bagshaw, D. Baguley, L. Bailey, J.K. Baillie, S. Bain, M. Bakali, M. Bakau, E. Baldry, D. Baldwin, M. Baldwin, C. Ballard, A. Banerjee, B. Bang, R.E. Barker, L. Barman, S. Barratt, F. Barrett, D. Basire, N. Basu, M. Bates, A. Bates, R. Batterham, H. Baxendale, H. Bayes, M. Beadsworth, P. Beckett, M. Beggs, M. Begum, P. Beirne, D. Bell, R. Bell, K. Bennett, E. Beranova, A. Bermperi, A. Berridge, C. Berry, S. Betts, E. Bevan, K. Bhui, M. Bingham, K. Birchall, L. Bishop, K. Bisnauthsing, J. Blaikely, A. Bloss, A. Bolger, C.E. Bolton, J. Bonnington, A. Botkai, C. Bourne, M. Bourne, K. Bramham, L. Brear, G. Breen, J. Breeze, A. Briggs, E. Bright, C.E. Brightling, S. Brill, K. Brindle, L. Broad, A. Broadley, C. Brookes, M. Broome, A. Brown, J. Brown, J.S. Brown, M. Brown, V. Brown, T. Brugha, N. Brunskill, M. Buch, P. Buckley, A. Bularga, E. Bullmore, L. Burden, T. Burdett, D. Burn, G. Burns, A. Burns, J. Busby, R. Butcher, A. Butt, S. Byrne, P. Cairns, P.C. Calder, E. Calvelo, H. Carborn, B. Card, C. Carr, L. Carr, G. Carson, P. Carter, A. Casey, M. Cassar, J. Cavanagh, M. Chablani, T. Chalder, J.D. Chalmers, R.C. Chambers, F. Chan, K.M. Channon, K. Chapman, A. Charalambou, N. Chaudhuri, A. Checkley, J. Chen, Y. Cheng, L. Chetham, C. Childs, E.R. Chilvers, H. Chinoy, A. Chiribiri, K. Chong-James, G. Choudhury, N. Choudhury, P. Chowienczyk, C. Christie, M. Chrystal, D. Clark, C. Clark, J. Clarke, S. Clohisey, G. Coakley, Z. Coburn, S. Coetzee, J. Cole, C. Coleman, F. Conneh, D. Connell, B. Connolly, L. Connor, A. Cook, B. Cooper, J. Cooper, S. Cooper, D. Copeland, T. Cosier, M. Coulding, C. Coupland, E. Cox, T. Craig, P. Crisp, D. Cristiano, M.G. Crooks, A. Cross, I. Cruz, P. Cullinan, D. Cuthbertson, L. Daines, M. Dalton, P. Daly, A. Daniels, P. Dark, J. Dasgin, A. David, C. David, E. Davies, F. Davies, G. Davies, G.A. Davies, K. Davies, M.J. Davies, J. Dawson, E. Daynes, A. De Soyza, B. Deakin, A. Deans, C. Deas, J. Deery, S. Defres, A. Dell, K. Dempsey, E. Denneny, J. Dennis, A. Dewar, R. Dharmagunawardena, N. Diar-Bakerly, C. Dickens, A. Dipper, S. Diver, S.N. Diwanji, M. Dixon, R. Djukanovic, H. Dobson, S.L. Dobson, A.B. Docherty, A. Donaldson, T. Dong, N. Dormand, A. Dougherty, R. Dowling, S. Drain, K. Draxlbauer, K. Drury, P. Dulawan, A. Dunleavy, S. Dunn, C. Dupont, J. Earley, N. Easom, C. Echevarria, S. Edwards, C. Edwardson, H. El-Taweel, A. Elliott, K. Elliott, Y. Ellis, A. Elmer, O. Elneima, D. Evans, H. Evans, J. Evans, R. Evans, R.A. Evans, R.I. Evans, T. Evans, C. Evenden, L. Evison, L. Fabbri, S. Fairbairn, A. Fairman, K. Fallon, D. Faluyi, C. Favager, T. Fayzan, J. Featherstone, T. Felton, J. Finch, S. Finney, J. Finnigan, L. Finnigan, H. Fisher, S. Fletcher, R. Flockton, M. Flynn, H. Foot, D. Foote, A. Ford, D. Forton, E. Fraile, C. Francis, R. Francis, S. Francis, A. Frankel, E. Fraser, R. Free, N. French, X. Fu, J. Fuld, J. Furniss, L. Garner, N. Gautam, J.R. Geddes, J. George, P. George, M. Gibbons, M. Gill, L. Gilmour, F. Gleeson, J. Glossop, S. Glover, N. Goodman, C. Goodwin, B. Gooptu, H. Gordon, T. Gorsuch, M. Greatorex, P.L. Greenhaff, W. Greenhalf, A. Greenhalgh, N.J. Greening, J. Greenwood, H. Gregory, R. Gregory, D. Grieve, D. Griffin, L. Griffiths, A-M. Guerdette, B. Guillen Guio, M. Gummadi, A. Gupta, S. Gurram, E. Guthrie, Z. Guy, H. H Henson, K. Hadley, A. Haggar, K. Hainey, B. Hairsine, P. Haldar, I. Hall, L. Hall, M. Halling-Brown, R. Hamil, A. Hancock, K. Hancock, N.A. Hanley, S. Haq, H.E. Hardwick, E. Hardy, T. Hardy, B. Hargadon, K. Harrington, E. Harris, V.C. Harris, E.M. Harrison, P. Harrison, N. Hart, A. Harvey, M. Harvey, M. Harvie, L. Haslam, M. Havinden-Williams, J. Hawkes, N. Hawkings, J. Haworth, A. Hayday, M. Haynes, J. Hazeldine, T. Hazelton, L.G. Heaney, C. Heeley, J.L. Heeney, M. Heightman, S. Heller, M. Henderson, L. Hesselden, M. Hewitt, V. Highett, T. Hillman, T. Hiwot, L.P. Ho, A. Hoare, M. Hoare, J. Hockridge, P. Hogarth, A. Holbourn, S. Holden, L. Holdsworth, D. Holgate, M. Holland, L. Holloway, K. Holmes, M. Holmes, B. Holroyd-Hind, L. Holt, A. Hormis, A. Horsley, A. Hosseini, M. Hotopf, L. Houchen-Wolloff, K. Howard, L.S. Howard, A. Howell, E. Hufton, A.D. Hughes, J. Hughes, R. Hughes, A. Humphries, N. Huneke, E. Hurditch, J. Hurst, M. Husain, T. Hussell, J. Hutchinson, W. Ibrahim, F. Ilyas, J. Ingham, L. Ingram, D. Ionita, K. Isaacs, K. Ismail, T. Jackson, J. Jacob, W.Y. James, W. Jang, C. Jarman, I. Jarrold, H. Jarvis, R. Jastrub, B. Jayaraman, R.G. Jenkins, P. Jezzard, K. Jiwa, C. Johnson, S. Johnson, D. Johnston, C.J. Jolley, D. Jones, G. Jones, H. Jones, I. Jones, L. Jones, M.G. Jones, S. Jones, S. Jose, T. Kabir, G. Kaltsakas, V. Kamwa, N. Kanellakis, s. Kaprowska, Z. Kausar, N. Keenan, S. Kelly, G. Kemp, S. Kerr, H. Kerslake, A.L. Key, F. Khan, K. Khunti, S. Kilroy, B. King, C. King, L. Kingham, J. Kirk, P. Kitterick, P. Klenerman, L. Knibbs, S. Knight, A. Knighton, O. 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Subjects
SARS-CoV-2 variants, Mucosal immunity, Nasal antibody, SDG 3 - Good Health and Well-being, Biochemistry, Genetics and Molecular Biology(all), SARS-CoV-2 immunity, Vaccination, COVID-19, General Medicine, Convalescent, General Biochemistry, Genetics and Molecular Biology
Abstract

Data sharing statement This is an Open Access article under the CC BY 4.0 license The ISARIC4C protocol, data sharing and publication policy are available at https://isaric4c.net. ISARIC4C's Independent Data and Material Access Committee welcomes applications for access to data and materials (https://isaric4c.net). The PHOSP-COVID protocol, consent form, definition and derivation of clinical characteristics and outcomes, training materials, regulatory documents, information about requests for data access, and other relevant study materials are available online: https://phosp.org/resource/. Access to these materials can be granted by contacting phosp@leicester.ac.uk and Phospcontracts@leicester.ac.uk. All data used in this study is available within ODAP and accessible under reasonable request. Data access criteria and information about how to request access is available online: https://phosp.org/resource/. If criteria are met and a request is made, access can be gained by signing the eDRIS user agreement. Supplementary data are available online at https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(22)00584-9/fulltext#supplementaryMaterial . Copyright © 2022 The Author(s). Background: Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. Methods: In this follow up study, plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. Findings: Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months (p < 0.0001). Nasal and plasma anti-S IgG remained elevated for at least 12 months (p < 0.0001) with plasma neutralising titres that were raised against all variants compared to controls (p < 0.0001). Of 323 with complete data, 307 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal (1.46-fold change after 10 months, p = 0.011) and the median remained below the positive threshold determined by pre-pandemic controls. Samples 12 months after admission showed no association between nasal IgA and plasma IgG anti-S responses (R = 0.05, p = 0.18), indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. Interpretation: The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity. Funding: This study has been supported by ISARIC4C and PHOSP-COVID consortia. ISARIC4C is supported by grants from the National Institute for Health and Care Research and the Medical Research Council. Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research. The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research. The funders were not involved in the study design, interpretation of data or the writing of this manuscript. This work is supported by the following grants: The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute for Health and Care Research (grant references: MR/V027859/1 and COV0319). ISARIC4C is supported by grants from the National Institute for Health and Care Research (award CO-CIN-01) and the Medical Research Council (grant MC_PC_19059) Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research (grant reference: C18616/A25153). Other grants which have supported this work include: the UK Coronavirus Immunology Consortium [funder reference:1257927], the Imperial Biomedical Research Centre (NIHR Imperial BRC, grant IS-BRC-1215-20013), the Health Protection Research Unit (HPRU) in Respiratory Infections at Imperial College London and NIHR HPRU in Emerging and Zoonotic Infections at University of Liverpool, both in partnership with Public Health England, [NIHR award 200907], Wellcome Trust and Department for International Development [215091/Z/18/Z], Health Data Research UK (HDR UK) [grant code: 2021.0155], Medical Research Council [grant code: MC_UU_12014/12], and NIHR Clinical Research Network for providing infrastructure support for this research. FL is supported by an MRC clinical training fellowship [award MR/W000970/1]. LPH is supported by Oxford NIHR Biomedical Research Centre. AART is supported by a BHF Intermediate Clinical Fellowship (FS/18/13/33281). SLRJ receives support from UKRI, GCRF, Rosetrees Trust, BHIVA, EDCTP, Globvac. JDC has grants from AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Gilead Sciences, Grifols, Novartis and Insmed. RAE holds a NIHR Clinician Scientist Fellowship (CS-2016-16-020). AH is currently supported by UK Research and Innovation. NIHR and NIHR Manchester BRC. BR receives support from BHF Oxford Centre of Research Excellence, NIHR Oxford BRC and MRC. SJD is funded by an NIHR Global Research Professorship [NIHR300791]. DW is supported by an NIHR Advanced Fellowship. AH has received support from MRC and the Coronavirus Immunology Consortium (MR/V028448/1). LVW has received support from UKRI, GSK/Asthma + Lung UK and NIHR for this study. MGS has received support from NIHR UK, MRC UK and Health Protection Research Unit in Emerging & Zoonotic Infections, University of Liverpool. JKB is supported by the Wellcome Trust (223164/Z/21/Z) and UKRI (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1, and MC_PC_20029). PJMO is supported by a NIHR Senior Investigator Award [award 201385]. LT is supported by the Wellcome Trust [clinical career development fellowship grant number 205228/Z/16/Z], the Centre of Excellence in Infectious Diseases Research (CEIDR) and the Alder Hey Charity.

Published
2022
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28. ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

Author

Abbas, Ali, Abdukahil, Sheryl Ann, Abdulkadir, Nurul Najmee, Abe, Ryuzo, Abel, Laurent, Absil, Lara, Jabal, Kamal Abu, Zayyad, Hiba Abu, Acharya, Subhash, Acker, Andrew, Adachi, Shingo, Adam, Elisabeth, Adriano, Enrico, Adrião, Diana, Ageel, Saleh Al, Ahmed, Shakeel, Aiello, Marina, Ainscough, Kate, Airlangga, Eka, Aisa, Tharwat, Hssain, Ali Ait, Tamlihat, Younes Ait, Akimoto, Takako, Akmal, Ernita, Qasim, Eman Al, Al-dabbous, Tala, Al-Fares, Abdulrahman, Alalqam, Razi, Alberti, Angela, Alegesan, Senthilkumar, Alegre, Cynthia, Alessi, Marta, Alex, Beatrice, Alexandre, Kévin, Alfoudri, Huda, Ali, Adam, Ali, Imran, Shah, Naseem Ali, Sheikh, Naseem Ali, Alidjnou, Kazali Enagnon, Aliudin, Jeffrey, Alkhafajee, Qabas, Allavena, Clotilde, Allou, Nathalie, Altaf, Aneela, Alves, João, Alves, João Melo, Alves, Rita, Amaral, Maria, Amira, Nur, Ammerlaan, Heidi, Ampaw, Phoebe, Andini, Roberto, Andrejak, Claire, Angheben, Andrea, Angoulvant, François, Ansart, Séverine, Anthonidass, Sivanesen, Antonelli, Massimo, de Brito, Carlos Alexandre Antunes, Anwar, Kazi Rubayet, Apriyana, Ardiyan, Arabi, Yaseen, Aragao, Irene, Arancibia, Francisco, Araujo, Carolline, Arcadipane, Antonio, Archambault, Patrick, Arenz, Lukas, Arlet, Jean-Benoît, Arnold-Day, Christel, Aroca, Ana, Arora, Lovkesh, Arora, Rakesh, Artaud-Macari, Elise, Aryal, Diptesh, Asaki, Motohiro, Asensio, Angel, Ashley, Elizabeth, Ashraf, Muhammad, Asif, Namra, Asim, Mohammad, Assie, Jean Baptiste, Asyraf, Amirul, Atique, Anika, Attanyake, A. M. Udara Lakshan, Auchabie, Johann, Aumaitre, Hugues, Auvet, Adrien, Axelsen, Eyvind W., Azemar, Laurène, Azoulay, Cecile, Bach, Benjamin, Bachelet, Delphine, Badr, Claudine, Bævre-Jensen, Roar, Baig, Nadia, Baillie, J. 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Karine, Bhatt, Amar, Bhavsar, Krishna, Bianchi, Isabella, Bianco, Claudia, Bidin, Farah Nadiah, Singh, Moirangthem Bikram, Bin Humaid, Felwa, Bin Kamarudin, Mohd Nazlin, Bissuel, François, Biston, Patrick, Bitker, Laurent, Bitton, Jonathan, Blanco-Schweizer, Pablo, Blier, Catherine, Bloos, Frank, Blot, Mathieu, Blumberg, Lucille, Boccia, Filomena, Bodenes, Laetitia, Bogaarts, Alice, Bogaert, Debby, Boivin, Anne-Hélène, Bolze, Pierre-Adrien, Bompart, François, Bonelli, Patrizia, Bonfasius, Aurelius, Borges, Diogo, Borie, Raphaël, Bosse, Hans Martin, Botelho-Nevers, Elisabeth, Bouadma, Lila, Bouchaud, Olivier, Bouchez, Sabelline, Bouhmani, Dounia, Bouhour, Damien, Bouiller, Kévin, Bouillet, Laurence, Bouisse, Camile, Bounphiengsy, Thipsavanh, Bountthasavong, Latsaniphone, Boureau, Anne-Sophie, Bourke, John, Bouscambert, Maude, Bousquet, Aurore, Bouziotis, Jason, Boxma, Bianca, Boyer-Besseyre, Marielle, Boylan, Maria, Bozza, Fernando Augusto, Braconnier, Axelle, Braga, Cynthia, Brandenburger, Timo, Monteiro, Filipa Brás, Brazzi, Luca, Breen, Dorothy, Breen, Patrick, Brickell, Kathy, Browne, Alex, Browne, Shaunagh, Brozzi, Nicolas, Brunvoll, Sonja Hjellegjerde, Brusse-Keizer, Marjolein, Buchtele, Nina, Buesaquillo, Christian, Bugaeva, Polina, Buisson, Marielle, Buonsenso, Danilo, Burhan, Erlina, Burrell, Aidan, Bustos, Ingrid G., Butnaru, Denis, Cabie, André, Cabral, Susana, Caceres, Eder, Cadoz, Cyril, Callahan, Mia, Calligy, Kate, Calvache, Jose Andres, Caminiti, Caterina, Camões, João, Campana, Valentine, Campbell, Paul, Campisi, Josie, Canepa, Cecilia, Cantero, Mireia, Caraux-Paz, Pauline, Cárcel, Sheila, Cardellino, Chiara Simona, Cardoso, Filipa, Cardoso, Filipe, Cardoso, Nelson, Cardoso, Sofia, Carelli, Simone, Carlacci, Francesca, Carlier, Nicolas, Carmoi, Thierry, Carney, Gayle, Carqueja, Inês, Carret, Marie-Christine, Carrier, François Martin, Carroll, Ida, Carson, Gail, Carvalho, Leonor, Casanova, Maire-Laure, Cascão, Mariana, Casey, Siobhan, Casimiro, José, Cassandra, Bailey, Castañeda, Silvia, Castanheira, Nidyanara, Castor-Alexandre, Guylaine, Castrillón, Henry, Castro, Ivo, Catarino, Ana, Catherine, François-Xavier, Cattaneo, Paolo, Cavalin, Roberta, Cavalli, Giulio Giovanni, Cavayas, Alexandros, Ceccato, Adrian, Cervantes-Gonzalez, Minerva, Chair, Anissa, Chakveatze, Catherine, Chan, Adrienne, Chand, Meera, Auger, Christelle Chantalat, Chapplain, Jean-Marc, Chas, Julie, Chatterjee, Allegra, Chaudry, Mobin, Iñiguez, Jonathan Samuel Chávez, Chen, Anjellica, Chen, Yih-Sharng, Cheng, Matthew Pellan, Cheret, Antoine, Chetta, Alfredo Antonio, Chiarabini, Thibault, Chica, Julian, Chidambaram, Suresh Kumar, Tho, Leong Chin, Chirouze, Catherine, Chiumello, Davide, Cho, Hwa Jin, Cho, Sung-Min, Cholley, Bernard, Chommanam, Danoy, Chopin, Marie-Charlotte, Chow, Ting Soo, Chow, Yock Ping, Christy, Nathaniel, Chua, Hiu Jian, Chua, Jonathan, Cidade, Jose Pedro, Herreros, José Miguel Cisneros, Citarella, Barbara Wanjiru, Ciullo, Anna, Clarke, Emma, Clarke, Jennifer, Granado, Rolando Claure-Del, Clohisey, Sara, Cobb, Perren J., Codan, Cassidy, Cody, Caitriona, Coelho, Alexandra, Coles, Megan, Colin, Gwenhaël, Collins, Michael, Colombo, Sebastiano Maria, Combs, Pamela, Connor, Marie, Conrad, Anne, Contreras, Sofía, Conway, Elaine, Cooke, Graham S., Copland, Mary, Cordel, Hugues, Corley, Amanda, Cornelis, Sabine, Cornet, Alexander Daniel, Corpuz, Arianne Joy, Cortegiani, Andrea, Corvaisier, Grégory, Costigan, Emma, Couffignal, Camille, Couffin-Cadiergues, Sandrine, Courtois, Roxane, Cousse, Stéphanie, Cregan, Rachel, D’Orleans, Charles Crepy, Croonen, Sabine, Crowl, Gloria, Crump, Jonathan, Cruz, Claudina, Berm, Juan Luis Cruz, Rojo, Jaime Cruz, Csete, Marc, Cucino, Alberto, Cullen, Ailbhe, Cummings, Matthew, Curley, Ger, Curley, Gerard, Curlier, Elodie, Curran, Colleen, Custodio, Paula, D’Amico, Federico, D’Aragon, Frédérick, D’Ortenzio, Eric, da Silva Filipe, Ana, Da Silveira, Charlene, Dabaliz, Al-Awwab, Dagens, Andrew, Dahl, John Arne, Dahly, Darren, Dalton, Heidi, Dalton, Jo, Daly, Seamus, Daneman, Nick, Daniel, Corinne, Dankwa, Emmanuelle A, Dantas, Jorge, de Boer, Mark, de Loughry, Gillian, de Mendoza, Diego, De Montmollin, Etienne, de Oliveira França, Rafael Freitas, Oliveira, Ana Isabel de Pinho, De Rosa, Rosanna, De Rose, Cristina, de Silva, Thushan, de Vries, Peter, Deacon, Jillian, Dean, David, Debard, Alexa, DeBenedictis, Bianca, Debray, Marie-Pierre, DeCastro, Nathalie, Dechert, William, Deconninck, Lauren, Decours, Romain, Defous, Eve, Delacroix, Isabelle, Delaveuve, Eric, Delavigne, Karen, Delfos, Nathalie M., Deligiannis, Ionna, Dell’Amore, Andrea, Delmas, Christelle, Delobel, Pierre, Delsing, Corine, Demonchy, Elisa, Denis, Emmanuelle, Deplanque, Dominique, Depuydt, Pieter, Desai, Mehul, Descamps, Diane, Desvallées, Mathilde, Dewayanti, Santi, Dhanger, Pathik, Diallo, Alpha, Diamantis, Sylvain, Dias, André, Dias, Andrea, Diaz, Juan Jose, Diaz, Priscila, Diaz, Rodrigo, Didier, Kévin, Diehl, Jean-Luc, Dieperink, Wim, Dimet, Jérôme, Dinot, Vincent, Diop, Fara, Diouf, Alphonsine, Dishon, Yael, Djossou, Félix, Docherty, Annemarie B., Doherty, Helen, Dondorp, Arjen M, Dong, Andy, Donnelly, Christl A., Donnelly, Maria, Donohue, Chloe, Donohue, Sean, Donohue, Yoann, Doran, Peter, Dorival, Céline, Douangdala, Phouvieng, Douglas, James Joshua, Douma, Renee, Dournon, Nathalie, Downer, Triona, Downey, Joanne, Downing, Mark, Drake, Tom, Driscoll, Aoife, Dror, Amiel A., Dryden, Murray, Fonseca, Claudio Duarte, Dubee, Vincent, Dubos, François, Dubot-Pérès, Audrey, Ducancelle, Alexandre, Duculan, Toni, Dudman, Susanne, Duggal, Abhijit, Dunand, Paul, Dunning, Jake, Duplaix, Mathilde, Durante-Mangoni, Emanuele, Durham III, Lucian, Dussol, Bertrand, Duthoit, Juliette, Duval, Xavier, Dyrhol-Riise, Anne Margarita, Ean, Sim Choon, Echeverria-Villalobos, Marco, Economopoulos, Giorgio, Edelstein, Michael, Egan, Siobhan, Eggesbø, Linn Margrete, Eira, Carla, El Sanharawi, Mohammed, Elapavaluru, Subbarao, Elharrar, Brigitte, Ellerbroek, Jacobien, Ellingjord-Dale, Merete, Eloy, Philippine, Elshazly, Tarek, Elyazar, Iqbal, Enderle, Isabelle, Endo, Tomoyuki, Eng, Chan Chee, Engelmann, Ilka, Enouf, Vincent, Epaulard, Olivier, Escher, Martina, Esperatti, Mariano, Esperou, Hélène, Esposito-Farese, Marina, Estevão, João, Etienne, Manuel, Ettalhaoui, Nadia, Everding, Anna Greti, Evers, Mirjam, Fabre, Isabelle, Fabre, Marc, Faheem, Amna, Fahy, Arabella, Fairfield, Cameron J., Fakar, Zul, Fareed, Komal, Faria, Pedro, Farooq, Ahmed, Fateena, Hanan, Fatoni, Arie Zainul, Faure, Karine, Favory, Raphaël, Fayed, Mohamed, Feely, Niamh, Feeney, Laura, Fernandes, Jorge, Fernandes, Marília Andreia, Fernandes, Susana, Ferrand, François-Xavier, Devouge, Eglantine Ferrand, Ferrão, Joana, Ferrari, Carlo, Ferraz, Mário, Ferreira, Benigno, Ferreira, Isabel, Ferreira, Sílvia, Ferrer-Roca, Ricard, Ferriere, Nicolas, Ficko, Céline, Figueiredo-Mello, Claudia, Finlayson, William, Fiorda, Juan, Flament, Thomas, Flateau, Clara, Fletcher, Tom, Florio, Letizia Lucia, Flynn, Brigid, Flynn, Deirdre, Fogliazza, Federica, Foley, Claire, Foley, Jean, Fomin, Victor, Fonseca, Tatiana, Fontela, Patricia, Forsyth, Simon, Foster, Denise, Foti, Giuseppe, Fourn, Erwan, Fowler, Robert A., Fraher, Marianne, Franch-Llasat, Diego, Fraser, Christophe, Fraser, John F, Freire, Marcela Vieira, Ribeiro, Ana Freitas, French, Craig, Friedrich, Caren, Fritz, Ricardo, Fry, Stéphanie, Fuentes, Nora, Fukuda, Masahiro, Argin, G, Gaborieau, Valérie, Gaci, Rostane, Gagliardi, Massimo, Gagnard, Jean-Charles, Gagné, Nathalie, Gagneux-Brunon, Amandine, Gaião, Sérgio, Skeie, Linda Gail, Gallagher, Phil, Curto, Elena Gallego, Gamble, Carrol, Gani, Yasmin, Garan, Arthur, Garcia, Rebekha, Barrio, Noelia García, Garcia-Diaz, Julia, Garcia-Gallo, Esteban, Garimella, Navya, Garofalo, Federica, Garot, Denis, Garrait, Valérie, Gauli, Basanta, Gault, Nathalie, Gavin, Aisling, Gavrylov, Anatoliy, Gaymard, Alexandre, Gebauer, Johannes, Geraud, Eva, Morlaes, Louis Gerbaud, Germano, Nuno, Ghisulal, Praveen Kumar, Ghosn, Jade, Giani, Marco, Giaquinto, Carlo, Gibson, Jess, Gigante, Tristan, Gilg, Morgane, Gilroy, Elaine, Giordano, Guillermo, Girvan, Michelle, Gissot, Valérie, Gitaka, Jesse, Giwangkancana, Gezy, Glikman, Daniel, Glybochko, Petr, Gnall, Eric, Goco, Geraldine, Goehringer, François, Goepel, Siri, Goffard, Jean-Christophe, Goh, Jin Yi, Golob, Jonathan, Gomes, Rui, Gomez, Kyle, Gómez-Junyent, Joan, Gominet, Marie, Gonçalves, Bronner P., Gonzalez, Alicia, Gordon, Patricia, Gorelik, Yanay, Gorenne, Isabelle, Goubert, Laure, Goujard, Cécile, Goulenok, Tiphaine, Grable, Margarite, Graf, Jeronimo, Grandin, Edward Wilson, Granier, Pascal, Grasselli, Giacomo, Grazioli, Lorenzo, Green, Christopher A., Greene, Courtney, Greenhalf, William, Greffe, Segolène, Grieco, Domenico Luca, Griffee, Matthew, Griffiths, Fiona, Grigoras, Ioana, Groenendijk, Albert, Lordemann, Anja Grosse, Gruner, Heidi, Gu, Yusing, Guarracino, Fabio, Guedj, Jérémie, Guego, Martin, Guellec, Dewi, Guerguerian, Anne-Marie, Guerreiro, Daniela, Guery, Romain, Guillaumot, Anne, Guilleminault, Laurent, de Castro, Maisa Guimarães, Guimard, Thomas, Haalboom, Marieke, Haber, Daniel, Habraken, Hannah, Hachemi, Ali, Hackmann, Amy, Hadri, Nadir, Haidri, Fakhir, Hakak, Sheeba, Hall, Adam, Hall, Matthew, Halpin, Sophie, Hameed, Jawad, Hamer, Ansley, Hamers, Raph L., Hamidfar, Rebecca, Hammarström, Bato, Hammond, Terese, Han, Lim Yuen, Haniffa, Rashan, Hao, Kok Wei, Hardwick, Hayley, Harrison, Ewen M., Harrison, Janet, Harrison, Samuel Bernard Ekow, Hartman, Alan, Hasan, Mohd Shahnaz, Hashmi, Junaid, Hashmi, Madiha, Hayes, Ailbhe, Hays, Leanne, Heerman, Jan, Heggelund, Lars, Hendry, Ross, Hennessy, Martina, Henriquez-Trujillo, Aquiles, Hentzien, Maxime, Hernandez-Montfort, Jaime, Herr, Daniel, Hershey, Andrew, Hesstvedt, Liv, Hidayah, Astarini, Higgins, Dawn, Higgins, Eibhilin, Hinchion, Rita, Hinton, Samuel, Hiraiwa, Hiroaki, Hirkani, Haider, Hitoto, Hikombo, Ho, Antonia, Ho, Yi Bin, Hoctin, Alexandre, Hoffmann, Isabelle, Hoh, Wei Han, Hoiting, Oscar, Holt, Rebecca, Holter, Jan Cato, Horby, Peter, Horcajada, Juan Pablo, Hoshino, Koji, Hoshino, Kota, Houas, Ikram, Hough, Catherine L., Houltham, Stuart, Hsu, Jimmy Ming-Yang, Hulot, Jean-Sébastien, Huo, Stella, Hussain, Iqbal, Ijaz, Samreen, Illes, Hajnal-Gabriela, Imbert, Patrick, Imran, Mohammad, Sikander, Rana Imran, Imtiaz, Aftab, Inácio, Hugo, Dominguez, Carmen Infante, Ing, Yun Sii, Iosifidis, Elias, Ippolito, Mariachiara, Isgett, Sarah, Isidoro, Tiago, Ismail, Nadiah, Isnard, Margaux, Istre, Mette Stausland, Itai, Junji, Ito, Asami, Ivulich, Daniel, Jaafar, Danielle, Jaafoura, Salma, Jabot, Julien, Jackson, Clare, Jamieson, Nina, Jaquet, Pierre, Jassat, Waasila, Jaud-Fischer, Coline, Jaureguiberry, Stéphane, Javidfar, Jeffrey, Jaworsky, Denise, Jego, Florence, Jelani, Anilawati Mat, Jenum, Synne, Jimbo-Sotomayor, Ruth, Joe, Ong Yiaw, García, Ruth N. Jorge, Jørgensen, Silje Bakken, Joseph, Cédric, Joseph, Mark, Joshi, Swosti, Jourdain, Mercé, Jouvet, Philippe, June, Jennifer, Jung, Anna, Jung, Hanna, Juzar, Dafsah, Kafif, Ouifiya, Kaguelidou, Florentia, Kaisbain, Neerusha, Kaleesvran, Thavamany, Kali, Sabina, Kalicinska, Alina, Kalleberg, Karl Trygve, Kalomoiri, Smaragdi, Kamaluddin, Muhammad Aisar Ayadi, Kamaruddin, Zul Amali Che, Kamarudin, Nadiah, Kamineni, Kavita, Kandamby, Darshana Hewa, Kandel, Chris, Kang, Kong Yeow, Kanwal, Darakhshan, Kanyawati, Dyah, Karpayah, Pratap, Karsies, Todd, Kartsonaki, Christiana, Kasugai, Daisuke, Kataria, Anant, Katz, Kevin, Kaur, Aasmine, Kawasaki, Tatsuya, Kay, Christy, Keane, Hannah, Keating, Seán, Kedia, Pulak, Kelly, Andrea, Kelly, Aoife, Kelly, Claire, Kelly, Niamh, Kelly, Sadie, Kelly, Yvelynne, Kelsey, Maeve, Kennedy, Ryan, Kennon, Kalynn, Keomany, Sommay, Kernan, Maeve, Kerroumi, Younes, Keshav, Sharma, Kestelyn, Evelyne, Khalid, Imrana, Khalid, Osama, Khalil, Antoine, Khan, Coralie, Khan, Irfan, Khan, Quratul Ain, Khanal, Sushil, Khatak, Abid, Khawaja, Amin, Kherajani, Krish, Kho, Michelle E., Khoo, Denisa, Khoo, Ryan, Khoo, Saye, Khoso, Nasir, Kiat, Khor How, Kida, Yuri, Kihuga, Harrison, Kiiza, Peter, Granerud, Beathe Kiland, Kildal, Anders Benjamin, Kim, Jae Burm, Kimmoun, Antoine, Kindgen-Milles, Detlef, King, Alexander, Kitamura, Nobuya, Kjetland, Eyrun Floerecke Kjetland, Klenerman, Paul, Klont, Rob, Bekken, Gry Kloumann, Knight, Stephen R, Kobbe, Robin, Kodippily, Chamira, Vasconcelos, Malte Kohns, Koirala, Sabin, Komatsu, Mamoru, Korten, Volkan, Kosgei, Caroline, Kpangon, Arsène, Krawczyk, Karolina, Krishnan, Sudhir, Krishnan, Vinothini, Kruglova, Oksana, Kumar, Deepali, Kumar, Ganesh, Kumar, Mukesh, Vecham, Pavan Kumar, Kuriakose, Dinesh, Kurtzman, Ethan, Kusumastuti, Neurinda Permata, Kutsogiannis, Demetrios, Kutsyna, Galyna, Kyriakoulis, Konstantinos, L’Her, Erwan, Lachatre, Marie, Lacoste, Marie, Laffey, John G., Lagrange, Marie, Laine, Fabrice, Lairez, Olivier, Lakhey, Sanjay, Lalueza, Antonio, Lambert, Marc, Lamontagne, François, Langelot-Richard, Marie, Langlois, Vincent, Lantang, Eka Yudha, Lanza, Marina, Laouénan, Cédric, Laribi, Samira, Lariviere, Delphine, Lasry, Stéphane, Lath, Sakshi, Latif, Naveed, Launay, Odile, Laureillard, Didier, Lavie-Badie, Yoan, Law, Andrew, Law, Andy, Lawrence, Cassie, Lawrence, Teresa, Le, Minh, Le Bihan, Clément, Le Bris, Cyril, Le Falher, Georges, Le Fevre, Lucie, Le Hingrat, Quentin, Le Maréchal, Marion, Le Mestre, Soizic, Le Moal, Gwenaël, Le Moing, Vincent, Le Nagard, Hervé, Le Turnier, Paul, Leal, Ema, Santos, Marta Leal, Lee, Biing Horng, Lee, Heng Gee, Lee, James, Lee, Su Hwan, Lee, Todd C., Lee, Yi Lin, Leeming, Gary, Lefebvre, Bénédicte, Lefebvre, Laurent, Lefevre, Benjamin, LeGac, Sylvie, Lelievre, Jean-Daniel, Lellouche, François, Lemaignen, Adrien, Lemee, Véronique, Lemeur, Anthony, Lemmink, Gretchen, Lene, Ha Sha, Lennon, Jenny, León, Rafael, Leone, Marc, Leone, Michela, Lepiller, Quentin, Lescure, François-Xavier, Lesens, Olivier, Lesouhaitier, Mathieu, Lester-Grant, Amy, Letizia, Andrew, Levy, Bruno, Levy, Yves, Levy-Marchal, Claire, Lewandowska, Katarzyna, Bassi, Gianluigi Li, Liang, Janet, Liaquat, Ali, Liegeon, Geoffrey, Lim, Kah Chuan, Lim, Wei Shen, Lima, Chantre, Lina, Bruno, Lina, Lim, Lind, Andreas, Lingad, Maja Katherine, Lingas, Guillaume, Lion-Daolio, Sylvie, Lissauer, Samantha, Liu, Keibun, Livrozet, Marine, Lizotte, Patricia, Loforte, Antonio, Lolong, Navy, Loon, Leong Chee, Lopes, Diogo, Lopez-Colon, Dalia, Loschner, Anthony L., Loubet, Paul, Loufti, Bouchra, Louis, Guillame, Lourenco, Silvia, Lovelace-Macon, Lara, Low, Lee Lee, Lowik, Marije, Loy, Jia Shyi, Lucet, Jean Christophe, Bermejo, Carlos Lumbreras, Luna, Carlos M., Lungu, Olguta, Luong, Liem, Luque, Nestor, Luton, Dominique, Lwin, Nilar, Lyons, Ruth, Maasikas, Olavi, Mabiala, Oryane, MacDonald, Sarah, Machado, Moïse, Macheda, Gabriel, Sanchez, Juan Macias, Madhok, Jai, Madiha, Hashmi, de la Calle, Guillermo Maestro, Magara, Jacob, Maglietta, Giuseppe, Mahieu, Rafael, Mahy, Sophie, Maia, Ana Raquel, Maier, Lars S., Maillet, Mylène, Maitre, Thomas, Majori, Maria, Malfertheiner, Maximilian, Malik, Nadia, Mallon, Paddy, Maltez, Fernando, Malvy, Denis, Mammi, Patrizia, Manda, Victoria, Mandei, Jose M., Mandelbrot, Laurent, Manetta, Frank, Mankikian, Julie, Manning, Edmund, Manuel, Aldric, Malaque, Ceila Maria Sant’Ana, Marino, Daniel, Marino, Flávio, Markowicz, Samuel, Eid, Charbel Maroun, Marques, Ana, Marquis, Catherine, Marsh, Brian, Marsh, Laura, Marshal, Megan, Marshall, John, Martelli, Celina Turchi, Martin, Dori-Ann, Martin, Emily, Martin-Blondel, Guillaume, Martin-Loeches, Ignacio, Martin-Quiros, Alejandro, Martinelli, Alessandra, Martinot, Martin, Martins, Ana, Martins, João, Martins, Nuno, Rego, Caroline Martins, Martucci, Gennaro, Martynenko, Olga, Marwali, Eva Miranda, Marzukie, Marsilla, Jimenez, Juan Fernado Masa, Maslove, David, Mason, Phillip, Mason, Sabina, Masood, Sobia, Nor, Basri Mat, Matan, Moshe, Fernandes, Henrique Mateus, Mathew, Meghena, Mathieu, Daniel, Mattei, Mathieu, Matulevics, Romans, Maulin, Laurence, Maxwell, Michael, Maynar, Javier, Mayxay, Mayfong, Mazzoni, Thierry, Mc Evoy, Natalie, Mc Sweeney, Lisa, McArthur, Colin, McCarthy, Aine, McCarthy, Anne, McCloskey, Colin, McConnochie, Rachael, McDermott, Sherry, McDonald, Sarah E., McElroy, Aine, McElwee, Samuel, McEneany, Victoria, McGeer, Allison, McKay, Chris, McKeown, Johnny, McLean, Kenneth A., McNally, Paul, McNicholas, Bairbre, McPartlan, Elaine, Meaney, Edel, Mear-Passard, Cécile, Mechlin, Maggie, Meher, Maqsood, Mehkri, Omar, Mele, Ferruccio, Melo, Luis, Memon, Kashif, Mendes, Joao, Menkiti, Ogechukwu, Menon, Kusum, Mentré, France, Mentzer, Alexander J., Mercier, Emmanuelle, Mercier, Noémie, Merckx, Antoine, Mergeay-Fabre, Mayka, Mergler, Blake, Merson, Laura, Meschi, Tiziana, Mesquita, António, Meta, Roberta, Metwally, Osama, Meybeck, Agnès, Meyer, Dan, Meynert, Alison M., Meysonnier, Vanina, Meziane, Amina, Mezidi, Mehdi, Michelagnoli, Giuliano, Michelanglei, Céline, Michelet, Isabelle, Mihelis, Efstathia, Mihnovit, Vladislav, Miranda-Maldonado, Hugo, Misnan, Nor Arisah, Mohamed, Nik Nur Eliza, Mohamed, Tahira Jamal, Moin, Asma, Molina, David, Molinos, Elena, Molloy, Brenda, Mone, Mary, Monteiro, Agostinho, Montes, Claudia, Montrucchio, Giorgia, Moore, Sarah, Moore, Shona C., Cely, Lina Morales, Moro, Lucia, Tutillo, Diego Rolando Morocho, Morton, Ben, Motherway, Catherine, Motos, Ana, Mouquet, Hugo, Perrot, Clara Mouton, Moyet, Julien, Mudara, Caroline, Mufti, Aisha Kalsoom, Muh, Ng Yong, Muhamad, Dzawani, Mullaert, Jimmy, Müller, Fredrik, Müller, Karl Erik, Munblit, Daniel, Muneeb, Syed, Munir, Nadeem, Munshi, Laveena, Murphy, Aisling, Murphy, Lorna, Murris, Marlène, Murthy, Srinivas, Musaab, Himed, Mutti, Carlotta, Muvindi, Himasha, Muyandy, Gugapriyaa, Myrodia, Dimitra Melia, Mohd-Hanafiah, Farah Nadia, Nagpal, Dave, Narasimhan, Mangala, Narayanan, Nageswaran, Khan, Rashid Nasim, Nazerali-Maitland, Alasdair, Neant, Nadège, Neb, Holger, Necsoi, Coca, Nekliudov, Nikita A., Nelwan, Erni, Neto, Raul, Neumann, Emily, Neves, Bernardo, Ng, Pauline Yeung, Ng, Wing Yiu, Nghi, Anthony, Ngure, Jane, Nguyen, Duc, Choileain, Orna Ni, Leathlobhair, Niamh Ni, Nichol, Alistair, Nitayavardhana, Prompak, Nonas, Stephanie, Noordin, Nurul Amani Mohd, Noret, Marion, Norharizam, Nurul Faten Izzati, Norman, Lisa, Notari, Alessandra, Noursadeghi, Mahdad, Nowicka, Karolina, Nowinski, Adam, Nseir, Saad, Nunez, Jose I., Nurnaningsih, Nurnaningsih, Nusantara, Dwi Utomo, Nyamankolly, Elsa, Nygaard, Anders Benteson, Brien, Fionnuala O., Callaghan, Annmarie O., O’Callaghan, Annmarie, O’Donnell, Max, O’Halloran, Sophie, O’Hearn, Katie, O’Neil, Conar, O’Shea, Linda, O’Sullivan, Miriam, Occhipinti, Giovanna, OConnor, Derbrenn, Ogston, Tawnya, Ogura, Takayuki, Oh, Tak-Hyuk, Ohshimo, Shinichiro, Oldakowska, Agnieszka, Oliveira, João, Oliveira, Larissa, Olliaro, Piero L., Ong, David S. Y., Ong, Jee Yan, Oosthuyzen, Wilna, Opavsky, Anne, Openshaw, Peter, Orakzai, Saijad, Orozco-Chamorro, Claudia Milena, Orquera, Andrés, Ortoleva, Jamel, Osatnik, Javier, Othman, Siti Zubaidah, Otiku, Paul, Ouamara, Nadia, Ouissa, Rachida, Owyang, Clark, Oziol, Eric, Pagadoy, Maïder, Pages, Justine, Palacios, Amanda, Palacios, Mario, Palmarini, Massimo, Panarello, Giovanna, Panda, Prasan Kumar, Paneru, Hem, Pang, Lai Hui, Panigada, Mauro, Pansu, Nathalie, Papadopoulos, Aurélie, Parducci, Paolo, Oña, Edwin Fernando Paredes, Parke, Rachael, Parker, Melissa, Parra, Briseida, Parrini, Vieri, Pasha, Taha, Pasquier, Jérémie, Pastene, Bruno, Patauner, Fabian, Patel, Drashti, Pathmanathan, Mohan Dass, Patrão, Luís, Patricio, Patricia, Patrier, Juliette, Patrizi, Laura, Patterson, Lisa, Pattnaik, Rajyabardhan, Paul, Christelle, Paul, Mical, Paulos, Jorge, Paxton, William A., Payen, Jean-François, Peariasamy, Kalaiarasu, Jiménez, Miguel Pedrera, Peek, Giles J., Peelman, Florent, Peiffer-Smadja, Nathan, Peigne, Vincent, Pejkovska, Mare, Pelosi, Paolo, Peltan, Ithan D., Pereira, Rui, Perez, Daniel, Periel, Luis, Perpoint, Thomas, Pesenti, Antonio, Pestre, Vincent, Petrou, Lenka, Petrov-Sanchez, Ventzislava, Petrovic, Michele, Pettersen, Frank Olav, Peytavin, Gilles, Pharand, Scott, Phonemixay, Ooyanong, Piagnerelli, Michael, Picard, Walter, Picone, Olivier, de Piero, Maria, Pierobon, Carola, Piersma, Djura, Pimentel, Carlos, Pinto, Raquel, Pires, Catarina, Pironneau, Isabelle, Piroth, Lionel, Pisi, Roberta, Pitaloka, Ayodhia, Pius, Riinu, Piva, Simone, Plantier, Laurent, Png, Hon Shen, Poissy, Julien, Pokeerbux, Ryadh, Pokorska-Spiewak, Maria, Poli, Sergio, Pollakis, Georgios, Ponscarme, Diane, Popielska, Jolanta, Porto, Diego Bastos, Post, Andra-Maris, Postma, Douwe F., Povoa, Pedro, Póvoas, Diana, Powis, Jeff, Prapa, Sofia, Praphasiri, Viladeth, Preau, Sébastien, Prebensen, Christian, Preiser, Jean-Charles, Prinssen, Anton, Pritchard, Mark G., Priyadarshani, Gamage Dona Dilanthi, Proença, Lucia, Pudota, Sravya, Puéchal, Oriane, Semedi, Bambang Pujo, Pulicken, Mathew, Puntoni, Matteo, Purcell, Gregory, Quesada, Luisa, Quinones-Cardona, Vilmaris, González, Víctor Quirós, Quist-Paulsen, Else, Quraishi, Mohammed, Rabaa, Maia, Rabaud, Christian, Rabindrarajan, Ebenezer, Rafael, Aldo, Rafiq, Marie, Ragazzo, Gabrielle, Rahardjani, Mutia, Ab Rahman, Ahmad Kashfi Haji, Rahman, Rozanah Abd, Rahutullah, Arsalan, Rainieri, Fernando, Rajahram, Giri Shan, Ramachandran, Pratheema, Ramakrishnan, Nagarajan, Ramanathan, Kollengode, Ramli, Ahmad Afiq, Rammaert, Blandine, Ramos, Grazielle Viana, Rampello, Anais, Rana, Asim, Rangappa, Rajavardhan, Ranjan, Ritika, Ranza, Elena, Rapp, Christophe, Rashan, Aasiyah, Rashan, Thalha, Rasheed, Ghulam, Rasmin, Menaldi, Rätsep, Indrek, Rau, Cornelius, Rausa, Francesco, Ravi, Tharmini, Raza, Ali, Real, Andre, Rebaudet, Stanislas, Redl, Sarah, Reeve, Brenda, Rehman, Attaur, Reid, Liadain, Reikvam, Dag Henrik, Reis, Renato, Rello, Jordi, Remppis, Jonathan, Remy, Martine, Ren, Hongru, Renk, Hanna, Resseguier, Anne-Sophie, Revest, Matthieu, Rewa, Oleksa, Reyes, Luis Felipe, Reyes, Tiago, Ribeiro, Maria Ines, Ricchiuto, Antonia, Richardson, David, Richardson, Denise, Richier, Laurent, Ridzuan, Siti Nurul Atikah Ahmad, Riera, Jordi, Rios, Ana L., Rishu, Asgar, Rispal, Patrick, Risso, Karine, Nuñez, Maria Angelica Rivera, Rizer, Nicholas, Robba, Chiara, Roberto, André, Roberts, Stephanie, Robertson, David L., Robineau, Olivier, Roche-Campo, Ferran, Rodari, Paola, Rodeia, Simão, Abreu, Julia Rodriguez, Roessler, Bernhard, Roger, Claire, Roger, Pierre-Marie, Roilides, Emmanuel, Rojek, Amanda, Romaru, Juliette, Roncon-Albuquerque Jr, Roberto, Roriz, Mélanie, Rosa-Calatrava, Manuel, Rose, Michael, Rosenberger, Dorothea, Roslan, Nurul Hidayah Mohammad, Rossanese, Andrea, Rossetti, Matteo, Rossi, Sandra, Rossignol, Bénédicte, Rossignol, Patrick, Rousset, Stella, Roy, Carine, Roze, Benoît, Rusmawatiningtyas, Desy, Russell, Clark D., Ryan, Maeve, Ryan, Maria, Ryckaert, Mazankowski Heart Institute, Ryckaert, Steffi, Holten, Aleksander Rygh, Saba, Isabela, Sacchelli, Luca, Sadaf, Sairah, Sadat, Musharaf, Sahraei, Valla, Saidani, Nadia, Saint-Gilles, Maximilien, Sakiyalak, Pranya, Salahuddin, Nawal, Salazar, Leonardo, Saleem, Jodat, Saleh, Nazal, Sales, Gabriele, Sallaberry, Stéphane, Gandonniere, Charlotte Salmon, Salvator, Hélène, Sanchez, Olivier, Choez, Xavier Sánchez, de Oliveira, Kizy Sanchez, Sanchez-Miralles, Angel, Sancho-Shimizu, Vanessa, Sandhu, Gyan, Sandhu, Zulfiqar, Sandrine, Pierre-François, Sandulescu, Oana, Santos, Marlene, Sarfo-Mensah, Shirley, Banheiro, Bruno Sarmento, Sarmiento, Iam Claire E., Sarton, Benjamine, Satya, Ankana, Satyapriya, Sree, Satyawati, Rumaisah, Saviciute, Egle, Savvidou, Parthena, Saw, Yen Tsen, Schaffer, Justin, Schermer, Tjard, Scherpereel, Arnaud, Schneider, Marion, Schroll, Stephan, Schwameis, Michael, Schwartz, Gary, Scott, Janet T., Scott-Brown, James, 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Skogen, Vegard, Smith, Sue, Smood, Benjamin, Smyth, Coilin, Smyth, Michelle, Snacken, Morgane, So, Dominic, Soh, Tze Vee, Solberg, Lene Bergendal, Solomon, Joshua, Solomon, Tom, Somers, Emily, Sommet, Agnès, Song, Myung Jin, Song, Rima, Song, Tae, Chia, Jack Song, Sonntagbauer, Michael, Soom, Azlan Mat, Søraas, Arne, Søraas, Camilla Lund, Sotto, Alberto, Soum, Edouard, Sousa, Ana Chora, Sousa, Marta, Uva, Maria Sousa, Souza-Dantas, Vicente, Sperry, Alexandra, Spinuzza, Elisabetta, Darshana, B. P. 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W., Tveita, Anders, Twardowski, Pawel, Uchiyama, Makoto, Udayanga, P G Ishara, Udy, Andrew, Ullrich, Roman, Uribe, Alberto, Usman, Asad, Uyeki, Timothy M., Vajdovics, Cristinava, Val-Flores, Luís, Valentini, Piero, Valle, Ana Luiza, Valran, Amélie, Valzano, Ilaria, Van de Velde, Stijn, van den Berge, Marcel, Van der Feltz, Machteld, van der Palen, Job, van der Valk, Paul, Van Der Vekens, Nicky, Van der Voort, Peter, Van Der Werf, Sylvie, van Dyk, Marlice, van Gulik, Laura, Van Hattem, Jarne, van Netten, Carolien, Van Twillert, Gitte, van Veen, Ilonka, Vanel, Noémie, Vanoverschelde, Henk, Varghese, Pooja, Varrone, Michael, Vasudayan, Shoban Raj, Vauchy, Charline, Veeran, Shaminee, Veislinger, Aurélie, Vencken, Sebastian, Ventura, Sara, Verbon, Annelies, Vickers, James, Vidal, José Ernesto, Vieira, César, Vijayan, Deepak, Villanueva, Joy Ann, Villar, Judit, Villeneuve, Pierre-Marc, Villoldo, Andrea, Van Vinh Chau, Nguyen, Vishwanathan, Gayatri, Visseaux, Benoit, Visser, Hannah, Vitiello, Chiara, Vongsouvath, Manivanh, Vonkeman, Harald, Vuotto, Fanny, Wahab, Noor Hidayu, Wahab, Suhaila Abdul, Wahid, Nadirah Abdul, Wainstein, Marina, Shukeri, Wan Fadzlina Wan Muhd, Wang, Chih-Hsien, Webb, Steve, Wei, Jia, Weil, Katharina, Wen, Tan Pei, Wesselius, Sanne, West, T. Eoin, Wham, Murray, Whelan, Bryan, White, Nicole, Wicky, Paul Henri, Wiedemann, Aurélie, Wijaya, Surya Otto, Wille, Keith, Willems, Suzette, Williams, Virginie, Wils, Evert-Jan, Wong, Calvin, Wong, Teck Fung, Wong, Xin Ci, Wong, Yew Sing, Wright, Natalie, Xian, Gan Ee, Xian, Lim Saio, Xuan, Kuan Pei, Xynogalas, Ioannis, Yacoub, Sophie, Yakop, Siti Rohani Binti Mohd, Yamazaki, Masaki, Yazdanpanah, Yazdan, Hing, Nicholas Yee Liang, Yelnik, Cécile, Yeoh, Chian Hui, Yerkovich, Stephanie, Yiaye, Touxiong, Yokoyama, Toshiki, Yonis, Hodane, Yousif, Obada, Yuliarto, Saptadi, Zaaqoq, Akram, Zabbe, Marion, Zacharowski, Kai, Zahid, Masliza, Zahran, Maram, Zaidan, Nor Zaila Binti, Zambon, Maria, Zambrano, Miguel, Zanella, Alberto, Zawadka, Konrad, Zaynah, Nurul, Zayyad, Hiba, Zoufaly, Alexander, Zucman, David, Fryer, Daniel Vidali, Duque, Sara, Fuentes, Yuli V., Baruch, Joaquin, Beane, Abi, Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), CHU Pontchaillou [Rennes], ARN régulateurs bactériens et médecine (BRM), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre National de Référence des virus des infections respiratoires (dont la grippe) - National Reference Center Virus Influenzae [Paris] (CNR - laboratoire coordonnateur), Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), University of La Sabana = Universitad de la Sabana, University of Queensland [Brisbane], University of British Columbia (UBC), University of Oxford, The investigators acknowledge: This work is part of the Grand Challenges ICODA pilot initiative, delivered by Health Data Research UK and funded by the Bill & Melinda Gates Foundation and the Minderoo Foundation. The philanthropic support of the donors to the University of Oxford’s COVID-19 Research Response Fund, UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z and 220757/Z/20/Z], the Bill & Melinda Gates Foundation [OPP1209135], the National Institute for Health Research (NIHR, award CO-CIN-01), the Medical Research Council (MRC, grant MC_PC_19059), 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 IS-BRC-1215-20013), NIHR Clinical Research Network (infrastructure support), CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and was coordinated out of Sunnybrook Research Institute, the endorsement of the Irish Critical Care- Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care- Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014-12], Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589], European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313], COVID clinical management team, AIIMS, Rishikesh, India, Cambridge NIHR Biomedical Research Centre, the dedication and hard work of the Groote Schuur Hospital Covid ICU Team, the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town, Wellcome Trust fellowship [205228/Z/16/Z], the Liverpool School of Tropical Medicine, the University of Oxford, the dedication and hard work of the Norwegian SARS-CoV-2 study team, the Research Council of Norway grant no 312780, a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner, 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, preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection, 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 (EQUS, CE170100009), the Prince Charles Hospital Foundation, Australia, UK Medical Research Council Clinical Research Training Fellowship MR/V001671/1, Instituto de Salud Carlos III, Ministerio de Ciencia, Spain, Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7, Firland Foundation, Shoreline, Washington, USA, the French COVID cohort (NCT04262921) is sponsored by INSERM and is funding by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n°20-0424). This work uses Data/Material provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The Data/materials used for this research were obtained from ISARIC4C. ISARIC4C Investigators collated the COVID-19 Clinical Information Network (CO-CIN) data., European Project: 101003589, H2020-SC1-PHE-CORONAVIRUS-2020,RECOVER(2020), Bill & Melinda Gates Foundation, Minderoo Foundation, Commonwealth Foundation, Wellcome Trust, National Institute for Health Research (UK), Medical Research Council (UK), University of Liverpool, Imperial College London, Canadian Institutes of Health Research, Sunnybrook Research Institute, Health Research Board (Ireland), European Commission, All India Institute of Medical Science, University of Cape Town, Research Council of Norway, Innovative Medicines Initiative, Governo Italiano, University of Queensland, Australian Research Council, Prince Charles Hospital Foundation, Instituto de Salud Carlos III, Institut National de la Santé et de la Recherche Médicale (France), Conselho Nacional de Desenvolvimento Científico e Tecnológico (Brasil), Garcia-Gallo, Esteban, Merson, Laura, Kennon, Kalynn, Kelly, Sadie, Citarella, Barbara Wanjiru, Fryer, Daniel Vidali, Shrapnel, Sally, Lee, Jame, Duque, Sara, Fuentes, Yuli V, Balan, Valeria, Smith, Sue, Wei, Jia, Gonçalves, Bronner P, Russell, Clark D, Sigfrid, Louise, Dagens, Andrew, Olliaro, Piero L, Baruch, Joaquin, Kartsonaki, Christiana, Dunning, Jake, Rojek, Amanda, Rashan, Aasiyah, Beane, Abi, Murthy, Sriniva, Reyes, Luis Felipe, ISARIC Clinical Characterization Group: Ali Abbas, Sheryl Ann Abdukahil, Nurul Najmee Abdulkadir, Ryuzo Abe, Laurent Abel, Lara Absil, Kamal Abu Jabal, Hiba Abu Zayyad, Subhash Acharya, Andrew Acker, Shingo Adachi, Elisabeth Adam, Enrico Adriano, Diana Adrião, Saleh Al Ageel, Shakeel Ahmed, Marina Aiello, Kate Ainscough, Eka Airlangga, Tharwat Aisa, Ali Ait Hssain, Younes Ait Tamlihat, Takako Akimoto, Ernita Akmal, Eman Al Qasim, Tala Al-Dabbous, Abdulrahman Al-Fares, Razi Alalqam, Angela Alberti, Senthilkumar Alegesan, Cynthia Alegre, Marta Alessi, Beatrice Alex, Kévin Alexandre, Huda Alfoudri, Adam Ali, Imran Ali, Naseem Ali Shah, Naseem Ali Sheikh, Kazali Enagnon Alidjnou, Jeffrey Aliudin, Qabas Alkhafajee, Clotilde Allavena, Nathalie Allou, Aneela Altaf, João Alves, João Melo Alves, Rita Alves, Maria Amaral, Nur Amira, Heidi Ammerlaan, Phoebe Ampaw, Roberto Andini, Claire Andrejak, Andrea Angheben, François Angoulvant, Séverine Ansart, Sivanesen Anthonidass, Massimo Antonelli, Carlos Alexandre Antunes de Brito, Kazi Rubayet Anwar, Ardiyan Apriyana, Yaseen Arabi, Irene Aragao, Francisco Arancibia, Carolline Araujo, Antonio Arcadipane, Patrick Archambault, Lukas Arenz, Jean-Benoît Arlet, Christel Arnold-Day, Ana Aroca, Lovkesh Arora, Rakesh Arora, Elise Artaud-Macari, Diptesh Aryal, Motohiro Asaki, Angel Asensio, Elizabeth Ashley, Muhammad Ashraf, Namra Asif, Mohammad Asim, Jean Baptiste Assie, Amirul Asyraf, Anika Atique, A M Udara Lakshan Attanyake, Johann Auchabie, Hugues Aumaitre, Adrien Auvet, Eyvind W Axelsen, Laurène Azemar, Cecile Azoulay, Benjamin Bach, Delphine Bachelet, Claudine Badr, Roar Bævre-Jensen, Nadia Baig, J Kenneth Baillie, J Kevin Baird, Erica Bak, Agamemnon Bakakos, Nazreen Abu Bakar, Andriy Bal, Mohanaprasanth Balakrishnan, Valeria Balan, Firouzé Bani-Sadr, Renata Barbalho, Nicholas Yuri Barbosa, Wendy S Barclay, Saef Umar Barnett, Michaela Barnikel, Helena Barrasa, Audrey Barrelet, Cleide Barrigoto, Marie Bartoli, Cheryl Bartone, Joaquín Baruch, Mustehan Bashir, Romain Basmaci, Muhammad Fadhli Hassin Basri, Denise Battaglini, Jules Bauer, Diego Fernando Bautista Rincon, Denisse Bazan Dow, Abigail Beane, Alexandra Bedossa, Ker Hong Bee, Husna Begum, Sylvie Behilill, Karine Beiruti, Albertus Beishuizen, Aleksandr Beljantsev, David Bellemare, Anna Beltrame, Beatriz Amorim Beltrão, Marine Beluze, Nicolas Benech, Lionel Eric Benjiman, Dehbia Benkerrou, Suzanne Bennett, Luís Bento, Jan-Erik Berdal, Delphine Bergeaud, Hazel Bergin, José Luis Bernal Sobrino, Giulia Bertoli, Lorenzo Bertolino, Simon Bessis, Adam Betz, Sybille Bevilcaqua, Karine Bezulier, Amar Bhatt, Krishna Bhavsar, Isabella Bianchi, Claudia Bianco, Farah Nadiah Bidin, Moirangthem Bikram Singh, Felwa Bin Humaid, Mohd Nazlin Bin Kamarudin, François Bissuel, Patrick Biston, Laurent Bitker, Jonathan Bitton, Pablo Blanco-Schweizer, Catherine Blier, Frank Bloos, Mathieu Blot, Lucille Blumberg, Filomena Boccia, Laetitia Bodenes, Alice Bogaarts, Debby Bogaert, Anne-Hélène Boivin, Pierre-Adrien Bolze, François Bompart, Patrizia Bonelli, Aurelius Bonfasius, Diogo Borges, Raphaël Borie, Hans Martin Bosse, Elisabeth Botelho-Nevers, Lila Bouadma, Olivier Bouchaud, Sabelline Bouchez, Dounia Bouhmani, Damien Bouhour, Kévin Bouiller, Laurence Bouillet, Camile Bouisse, Thipsavanh Bounphiengsy, Latsaniphone Bountthasavong, Anne-Sophie Boureau, John Bourke, Maude Bouscambert, Aurore Bousquet, Jason Bouziotis, Bianca Boxma, Marielle Boyer-Besseyre, Maria Boylan, Fernando Augusto Bozza, Axelle Braconnier, Cynthia Braga, Timo Brandenburger, Filipa Brás Monteiro, Luca Brazzi, Dorothy Breen, Patrick 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Sebastiano Maria Colombo, Pamela Combs, Marie Connor, Anne Conrad, Sofía Contreras, Elaine Conway, Graham S Cooke, Mary Copland, Hugues Cordel, Amanda Corley, Sabine Cornelis, Alexander Daniel Cornet, Arianne Joy Corpuz, Andrea Cortegiani, Grégory Corvaisier, Emma Costigan, Camille Couffignal, Sandrine Couffin-Cadiergues, Roxane Courtois, Stéphanie Cousse, Rachel Cregan, Charles Crepy D'Orleans, Sabine Croonen, Gloria Crowl, Jonathan Crump, Claudina Cruz, Juan Luis Cruz Berm, Jaime Cruz Rojo, Marc Csete, Alberto Cucino, Ailbhe Cullen, Matthew Cummings, Ger Curley, Gerard Curley, Elodie Curlier, Colleen Curran, Paula Custodio, Federico D'Amico, Frédérick D'Aragon, Eric D'Ortenzio, Ana da Silva Filipe, Charlene Da Silveira, Al-Awwab Dabaliz, Andrew Dagens, John Arne Dahl, Darren Dahly, Heidi Dalton, Jo Dalton, Seamus Daly, Nick Daneman, Corinne Daniel, Emmanuelle A Dankwa, Jorge Dantas, Mark de Boer, Gillian de Loughry, Diego de Mendoza, Etienne De Montmollin, Rafael Freitas de Oliveira 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Yuan Tee, João Teixeira, Sofia Tejada, Marie-Capucine Tellier, Sze Kye Teoh, Vanessa Teotonio, François Téoulé, Pleun Terpstra, Olivier Terrier, Nicolas Terzi, Hubert Tessier-Grenier, Adrian Tey, Alif Adlan Mohd Thabit, Anand Thakur, Zhang Duan Tham, Suvintheran Thangavelu, Vincent Thibault, Simon-Djamel Thiberville, Benoît Thill, Jananee Thirumanickam, Shaun Thompson, David Thomson, Emma C Thomson, Surain Raaj Thanga Thurai, Duong Bich Thuy, Ryan S Thwaites, Andrea Ticinesi, Paul Tierney, Vadim Tieroshyn, Peter S Timashev, Jean-François Timsit, Bharath Kumar Tirupakuzhi Vijayaraghavan, Noémie Tissot, Jordan Zhien Yang Toh, Maria Toki, Kristian Tonby, Sia Loong Tonnii, Antoni Torres, Margarida Torres, Rosario Maria Torres Santos-Olmo, Hernando Torres-Zevallos, Michael Towers, Tony Trapani, Théo Treoux, Huynh Trung Trieu, Cécile Tromeur, Ioannis Trontzas, Tiffany Trouillon, Jeanne Truong, Christelle Tual, Sarah Tubiana, Helen Tuite, Jean-Marie Turmel, Lance C W Turtle, Anders Tveita, Pawel Twardowski, Makoto Uchiyama, P G Ishara Udayanga, Andrew Udy, Roman Ullrich, Alberto Uribe, Asad Usman, Timothy M Uyeki, Cristinava Vajdovics, Luís Val-Flores, Piero Valentini, Ana Luiza Valle, Amélie Valran, Ilaria Valzano, Stijn Van de Velde, Marcel van den Berge, Machteld Van der Feltz, Job van der Palen, Paul van der Valk, Nicky Van Der Vekens, Peter Van der Voort, Sylvie Van Der Werf, Marlice van Dyk, Laura van Gulik, Jarne Van Hattem, Carolien van Netten, Gitte Van Twillert, Ilonka van Veen, Noémie Vanel, Henk Vanoverschelde, Pooja Varghese, Michael Varrone, Shoban Raj Vasudayan, Charline Vauchy, Shaminee Veeran, Aurélie Veislinger, Sebastian Vencken, Sara Ventura, Annelies Verbon, James Vickers, José Ernesto Vidal, César Vieira, Deepak Vijayan, Joy Ann Villanueva, Judit Villar, Pierre-Marc Villeneuve, Andrea Villoldo, Nguyen Van Vinh Chau, Gayatri Vishwanathan, Benoit Visseaux, Hannah Visser, Chiara Vitiello, Manivanh Vongsouvath, Harald Vonkeman, Fanny Vuotto, Noor Hidayu Wahab, Suhaila Abdul Wahab, Nadirah Abdul Wahid, Marina Wainstein, Wan Fadzlina Wan Muhd Shukeri, Chih-Hsien Wang, Steve Webb, Jia Wei, Katharina Weil, Tan Pei Wen, Sanne Wesselius, T Eoin West, Murray Wham, Bryan Whelan, Nicole White, Paul Henri Wicky, Aurélie Wiedemann, Surya Otto Wijaya, Keith Wille, Suzette Willems, Virginie Williams, Evert-Jan Wils, Calvin Wong, Teck Fung Wong, Xin Ci Wong, Yew Sing Wong, Natalie Wright, Gan Ee Xian, Lim Saio Xian, Kuan Pei Xuan, Ioannis Xynogalas, Sophie Yacoub, Siti Rohani Binti Mohd Yakop, Masaki Yamazaki, Yazdan Yazdanpanah, Nicholas Yee Liang Hing, Cécile Yelnik, Chian Hui Yeoh, Stephanie Yerkovich, Touxiong Yiaye, Toshiki Yokoyama, Hodane Yonis, Obada Yousif, Saptadi Yuliarto, Akram Zaaqoq, Marion Zabbe, Kai Zacharowski, Masliza Zahid, Maram Zahran, Nor Zaila Binti Zaidan, Maria Zambon, Miguel Zambrano, Alberto Zanella, Konrad Zawadka, Nurul Zaynah, Hiba Zayyad, Alexander Zoufaly, David Zucman, Durante-Mangoni, Emanuele, Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE), Microbes in Health and Disease (MHD), Garcia-Gallo E., Merson L., Kennon K., Kelly S., Citarella B. W. , Fryer D. V. , Shrapnel S., Lee J., Duque S., Fuentes Y. V. , et al., and Medical Microbiology & Infectious Diseases

Subjects
Eğitim, Social Sciences and Humanities, Information Security and Reliability, Social Sciences (SOC), Sosyal Bilimler ve Beşeri Bilimler, Epidemiology, EDUCATION & EDUCATIONAL RESEARCH, Temel Bilimler (SCI), BİLGİSAYAR BİLİMİ, BİLGİ SİSTEMLERİ, MATHEMATICS, Sociology, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Prospective Studies, COMPUTER SCIENCE, INFORMATION SYSTEMS, STATISTICS & PROBABILITY, Matematik, Bilgisayar Bilimi Uygulamaları, Computer Sciences, Bilgi Güvenliği ve Güvenilirliği, EĞİTİM VE EĞİTİM ARAŞTIRMASI, BİLGİ BİLİMİ VE KÜTÜPHANE BİLİMİ, Bilgi sistemi, Computer Science Applications, Kütüphane ve Bilgi Bilimleri, Hospitalization, Natural Sciences (SCI), Physical Sciences, Engineering and Technology, Sosyal Bilimler (SOC), Bilgisayar Bilimi, Statistics, Probability and Uncertainty, Information Systems, Human, Statistics and Probability, Humans, Pandemics, SARS-CoV-2, COVID-19, SOCIAL SCIENCES, GENERAL, Library and Information Sciences, Education, SDG 3 - Good Health and Well-being, Library Sciences, INFORMATION SCIENCE & LIBRARY SCIENCE, İstatistik ve Olasılık, Sosyal ve Beşeri Bilimler, Bilgisayar Bilimleri, Social Sciences & Humanities, Engineering, Computing & Technology (ENG), Sosyoloji, Pandemic, İSTATİSTİK & OLASILIK, Mühendislik, Bilişim ve Teknoloji (ENG), İstatistik, Olasılık ve Belirsizlik, Sosyal Bilimler Genel, COMPUTER SCIENCE, Prospective Studie, Fizik Bilimleri, Viral infection, Mühendislik ve Teknoloji, Kütüphanecilik
Abstract

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use., This work is part of the Grand Challenges ICODA pilot initiative, delivered by Health Data Research UK and funded by the Bill & Melinda Gates Foundation and the Minderoo Foundation. The philanthropic support of the donors to the University of Oxford’s COVID-19 Research Response Fund; UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z and 220757/Z/20/Z]; the Bill & Melinda Gates Foundation [OPP1209135]; the National Institute for Health Research (NIHR; award CO-CIN-01); the Medical Research Council (MRC; grant MC_PC_19059); 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 IS-BRC-1215-20013); NIHR Clinical Research Network (infrastructure support); CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and was coordinated out of Sunnybrook Research Institute; the endorsement of the Irish Critical Care- Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care- Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014-12]; Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589]; European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313]; COVID clinical management team, AIIMS, Rishikesh, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team; the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town; Wellcome Trust fellowship [205228/Z/16/Z]; the Liverpool School of Tropical Medicine; the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 study team; the Research Council of Norway grant no 312780; a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; 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; preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection; 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 (EQUS, CE170100009); the Prince Charles Hospital Foundation, Australia; UK Medical Research Council Clinical Research Training Fellowship MR/V001671/1; Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7; Firland Foundation, Shoreline, Washington, USA; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funding by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n°20-0424).

Published
2022
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