Corrected on Jun 1, 2020.
Source: the publisher, via Crossref · checked Oct 29, 2026 · Read the notice
- Corrected on Jun 1, 2020. Source: the publisher, via Crossref · Read the notice
Estimates of the severity of coronavirus disease 2019: a model-based analysis
Robert Verity, Lucy C Okell, Ilaria Dorigatti and 30 more
The Lancet Infectious Diseases · 2020 · CC BY
doi:10.1016/s1473-3099(20)30243-7
Abstract
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.
References 28
- 1.
WHO. Coronavirus disease 2019 (COVID-19) situation report—43. March 3, 2020.
Not in our library
- 2.
Chan JFW, Yuan S, Kok KH. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020;395:514-523.
Not in our library10.1016/s0140-6736(20)30154-9
- 3.
Chen N, Zhou M, Dong X. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507-513.
Not in our library10.1016/s0140-6736(20)30211-7
- 4.
Guan WJ, Ni ZY, Hu Y. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020.
Not in our library10.1056/nejmoa2002032
- 5.
Huang C, Wang Y, Li X. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497-506.
Not in our library10.1016/s0140-6736(20)30183-5
- 6.
Li Q, Guan X, Wu P. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. 2020.
Not in our library10.1056/nejmoa2001316
- 7.
Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua Liu Xing Bing Xue Za Zhi. 2020;41:145-151.
Not in our library
- 8.
WHO. Report of the WHO-China Joint Mission on coronavirus disease 2019 (COVID-19). Feb 28, 2020.
Not in our library
- 9.
Garske T, Legrand J, Donnelly CA. Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ. 2009;339.
Not in our library10.1136/bmj.b2840
- 10.
- 11.
Ghani AC, Donnelly CA, Cox DR. Methods for estimating the case fatality ratio for a novel, emerging infectious disease. Am J Epidemiol. 2005;162:479-486.
Not in our library10.1093/aje/kwi230
- 12.
Donnelly CA, Ghani AC, Leung GM. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet. 2003;361:1761-1766.
Not in our library10.1016/s0140-6736(03)13410-1
- 13.
- 14.
Mizumoto K, Kagaya K, Chowell G, Yoshida-Nakaadachi-cho U. Early epidemiological assessment of the transmission potential and virulence of 2019 novel coronavirus in Wuhan City: China, January–February, 2020. medRxiv. 2020.
Not in our library10.1101/2020.02.12.20022434
- 15.
Famulare M. 2019-nCoV: preliminary estimates of the confirmed-case-fatality-ratio and infection-fatality-ratio, and initial pandemic risk assessment. Feb 19, 2020.
Not in our library
- 16.
- 17.
Xu XW, Wu XX, Jiang XG. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ. 2020;368:m606.
Not in our library10.1136/bmj.m606
- 18.
Yang Y, Lu Q, Liu M. Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China. medRxiv. 2020.
Not in our library10.1101/2020.02.10.20021675
- 19.
Government of Japan. Ministry of Health, Labour and Welfare. 2020.
Not in our library
- 20.
National Bureau of Statistics of China. China statistical yearbook. 2018.
Not in our library
- 21.
Verity R, Winskill P. drjacoby.
Not in our library
- 22.
Griffin J, Ghani A. CASEFAT: Stata module for estimating the case fatality ratio of a new infectious disease. Boston College of Economics. May 7, 2009.
Not in our library
- 23.
Zhou F, Yu T, Du R. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020.
Not in our library10.1016/s0140-6736(20)30566-3
- 24.
- 25.
Lessler J, Salje H, Van Kerkhove MD. Estimating the severity and subclinical burden of middle east respiratory syndrome coronavirus infection in the Kingdom of Saudi Arabia. Am J Epidemiol. 2016;183:657-663.
Not in our library10.1093/aje/kwv452
- 26.
Riley S, Kwok KO, Wu KM. Epidemiological characteristics of 2009 (H1N1) pandemic influenza based on paired sera from a longitudinal community cohort study. PLoS Med. 2011;8.
Not in our library10.1371/journal.pmed.1000442
- 27.
- 28.
Bi Q, Wu Y, Mei S. Epidemiology and transmission of COVID-19 in Shenzhen China: analysis of 391 cases and 1,286 of their close contacts. medRxiv. 2020.
Not in our library10.1101/2020.03.03.20028423
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