S'abonner

Estimates of the severity of coronavirus disease 2019: a model-based analysis - 08/06/20

Doi : 10.1016/S1473-3099(20)30243-7 
Robert Verity, PhD a, , Lucy C Okell, PhD a, , Ilaria Dorigatti, PhD a, , Peter Winskill, PhD a, , Charles Whittaker, MSc a, , Natsuko Imai, PhD a, Gina Cuomo-Dannenburg, MMath a, Hayley Thompson, MPH a, Patrick G T Walker, PhD a, Han Fu, PhD a, Amy Dighe, MRes a, Jamie T Griffin, PhD b, Marc Baguelin, PhD a, Sangeeta Bhatia, PhD a, Adhiratha Boonyasiri, MD a, Anne Cori, PhD a, Zulma Cucunubá, PhD a, Rich FitzJohn, PhD a, Katy Gaythorpe, PhD a, Will Green, MSc a, Arran Hamlet, MSc a, Wes Hinsley, PhD a, Daniel Laydon, PhD a, Gemma Nedjati-Gilani, PhD a, Steven Riley, ProfDPhil a, Sabine van Elsland, PhD a, Erik Volz, PhD a, Haowei Wang, MSc a, Yuanrong Wang a, Xiaoyue Xi, MSc a, Christl A Donnelly, ProfScD a, c, Azra C Ghani, ProfPhD a, , Neil M Ferguson, ProfDPhil a, ,
a MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK 
b School of Mathematical Sciences, Queen Mary University of London, London, UK 
c Department of Statistics, University of Oxford, Oxford, UK 

* Correspondence to: Prof Azra Ghani, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London W2 1PG, UK MRC Centre for Global Infectious Disease Analysis Abdul Latif Jameel Institute for Disease and Emergency Analytics Imperial College London London W2 1PG UK ** Prof Neil Ferguson, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London W2 1PG, UK MRC Centre for Global Infectious Disease Analysis Abdul Latif Jameel Institute for Disease and Emergency Analytics Imperial College London London W2 1PG UK

Bienvenue sur EM-consulte, la référence des professionnels de santé.
Article gratuit.

Connectez-vous pour en bénéficier!

Summary

Background

In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases.

Methods

We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation.

Findings

Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–37·6) in those aged 80 years or older.

Interpretation

These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death.

Funding

UK Medical Research Council.

Le texte complet de cet article est disponible en PDF.

Plan


© 2020  The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Publié par Elsevier Masson SAS. Tous droits réservés.
Ajouter à ma bibliothèque Retirer de ma bibliothèque Imprimer
Export

    Export citations

  • Fichier

  • Contenu

Vol 20 - N° 6

P. 669-677 - juin 2020 Retour au numéro
Article précédent Article précédent
  • Centuries of sexually transmitted infections
  • Talha Burki
| Article suivant Article suivant
  • Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study
  • Joel R Koo, Alex R Cook, Minah Park, Yinxiaohe Sun, Haoyang Sun, Jue Tao Lim, Clarence Tam, Borame L Dickens

Bienvenue sur EM-consulte, la référence des professionnels de santé.

Mon compte


Plateformes Elsevier Masson

Déclaration CNIL

EM-CONSULTE.COM est déclaré à la CNIL, déclaration n° 1286925.

En application de la loi nº78-17 du 6 janvier 1978 relative à l'informatique, aux fichiers et aux libertés, vous disposez des droits d'opposition (art.26 de la loi), d'accès (art.34 à 38 de la loi), et de rectification (art.36 de la loi) des données vous concernant. Ainsi, vous pouvez exiger que soient rectifiées, complétées, clarifiées, mises à jour ou effacées les informations vous concernant qui sont inexactes, incomplètes, équivoques, périmées ou dont la collecte ou l'utilisation ou la conservation est interdite.
Les informations personnelles concernant les visiteurs de notre site, y compris leur identité, sont confidentielles.
Le responsable du site s'engage sur l'honneur à respecter les conditions légales de confidentialité applicables en France et à ne pas divulguer ces informations à des tiers.


Tout le contenu de ce site: Copyright © 2024 Elsevier, ses concédants de licence et ses contributeurs. Tout les droits sont réservés, y compris ceux relatifs à l'exploration de textes et de données, a la formation en IA et aux technologies similaires. Pour tout contenu en libre accès, les conditions de licence Creative Commons s'appliquent.