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Estimates of the severity of COVID-19 disease
Robert Verity; Lucy C Okell; Ilaria Dorigatti; Peter Winskill; Charles Whittaker; Natsuko Imai; Gina Cuomo-Dannenburg; Hayley Thompson; Patrick Walker; Han Fu; Amy Dighe; Jamie Griffin; Anne Cori; Marc Baguelin; Sangeeta Bhatia; Adhiratha Boonyasiri; Zulma M Cucunuba; Rich Fitzjohn; Katy A M Gaythorpe; Will Green; Arran Hamlet; Wes Hinsley; Daniel Laydon; Gemma Nedjati-Gilani; Steven Riley; Sabine van-Elsand; Erik Volz; Haowei Wang; Yuanrong Wang; Xiayoue Xi; Christl Donnelly; Azra Ghani; Neil Ferguson.
Afiliación
  • Robert Verity; Imperial College London
  • Lucy C Okell; Imperial College London
  • Ilaria Dorigatti; Imperial College London
  • Peter Winskill; Imperial College London
  • Charles Whittaker; Imperial College London
  • Natsuko Imai; Imperial College London
  • Gina Cuomo-Dannenburg; Imperial College London
  • Hayley Thompson; Imperial College London
  • Patrick Walker; Imperial College London
  • Han Fu; Imperial College London
  • Amy Dighe; Imperial College London
  • Jamie Griffin; Queen Mary University of London
  • Anne Cori; Imperial College London
  • Marc Baguelin; Imperial College London
  • Sangeeta Bhatia; Imperial College London
  • Adhiratha Boonyasiri; Imperial College London
  • Zulma M Cucunuba; Imperial College London
  • Rich Fitzjohn; Imperial College London
  • Katy A M Gaythorpe; Imperial College London
  • Will Green; Imperial College London
  • Arran Hamlet; Imperial College London
  • Wes Hinsley; Imperial College London
  • Daniel Laydon; Imperial College London
  • Gemma Nedjati-Gilani; Imperial College London
  • Steven Riley; Dept Inf Dis Epi, Imperial College
  • Sabine van-Elsand; Imperial College London
  • Erik Volz; Imperial College London
  • Haowei Wang; Imperial College London
  • Yuanrong Wang; Imperial College London
  • Xiayoue Xi; Imperial College London
  • Christl Donnelly; Imperial College London
  • Azra Ghani; Imperial College London
  • Neil Ferguson; Imperial College London
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20033357
ABSTRACT
BackgroundA range of case fatality ratio (CFR) estimates for COVID-19 have been produced that differ substantially in magnitude. MethodsWe used individual-case data from mainland China and cases detected outside mainland China to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the CFR by relating the aggregate distribution of cases by dates of onset to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for the demography of the population, and age- and location-based under-ascertainment. We additionally estimated the CFR from individual line-list data on 1,334 cases identified outside mainland China. We used data on the PCR prevalence in international residents repatriated from China at the end of January 2020 to obtain age-stratified estimates of the infection fatality ratio (IFR). Using data on age-stratified severity in a subset of 3,665 cases from China, we estimated the proportion of infections that will likely require hospitalisation. FindingsWe estimate the mean duration from onset-of-symptoms to death to be 17.8 days (95% credible interval, crI 16.9-19.2 days) and from onset-of-symptoms to hospital discharge to be 22.6 days (95% crI 21.1-24.4 days). We estimate a crude CFR of 3.67% (95% crI 3.56%-3.80%) in cases from mainland China. Adjusting for demography and under-ascertainment of milder cases in Wuhan relative to the rest of China, we obtain a best estimate of the CFR in China of 1.38% (95% crI 1.23%-1.53%) with substantially higher values in older ages. Our estimate of the CFR from international cases stratified by age (under 60 / 60 and above) are consistent with these estimates from China. We obtain an overall IFR estimate for China of 0.66% (0.39%-1.33%), again with an increasing profile with age. InterpretationThese early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and demonstrate a strong age-gradient in risk.
Licencia
cc_by_nc_nd
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint