The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China
Open accessAbstract
As of 29 February 2020 there were 79,394 confirmed cases and 2,838 deaths from COVID-19 in mainland China. Of these, 48,557 cases and 2,169 deaths occurred in the epicenter, Wuhan. A key public health priority during the emergence of a novel pathogen is estimating clinical severity, which requires properly adjusting for the case ascertainment rate and the delay between symptoms onset and death. Using public and published information, we estimate that the overall symptomatic case fatality risk (the probability of dying after developing symptoms) of COVID-19 in Wuhan was 1.4% (0.9–2.1%), which is substantially lower than both the corresponding crude or naïve confirmed case fatality risk (2,169/48,557 = 4.5%) and the approximator1 of deaths/deaths + recoveries (2,169/2,169 + 17,572 = 11%) as of 29 February 2020. Compared to those aged 30–59 years, those aged below 30 and above 59 years were 0.6 (0.3–1.1) and 5.1 (4.2–6.1) times more likely to die after developing symptoms. The risk of symptomatic infection increased with age (for example, at ~4% per year among adults aged 30–60 years). An estimation of the clinical severity of COVID-19, based on the data available so far, can help to inform the public health response during the ongoing SARS-CoV-2 pandemic.
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Cites
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
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Self-citation1918 Influenza: the Mother of All Pandemics
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Donnelly, C. A., Ghani, A. C., Leung, G. M., Hedley, A. J., Fraser, C., Riley, S., Abu-Raddad, L. J., Ho, L.-M., Thach, T.-Q., Chau, P., et al. (2003) · The Lancet · doi:10.1016/s0140-6736(03)13410-1 · Cited by 940 · cited 1×
“The mean time from onset to death is 20 (17) (18) (19) (20) (21) (22) (23) (24) days, with a standard deviation of 10 (7-14) days.”
Self-citationSecondary attack rate and superspreading events for SARS-CoV-2
Liu, Y., Eggo, R. M., & Kucharski, A. J. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30462-1 · Cited by 350
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Vaduganathan, M., Vardeny, O., Michel, T., McMurray, J. J. V., Pfeffer, M. A., & Solomon, S. D. (2020) · New England Journal of Medicine · doi:10.1056/nejmsr2005760 · Cited by 2,101
Covid-19: risk factors for severe disease and death
Jordan, R. E., Adab, P., & Cheng, K. K. (2020) · BMJ · doi:10.1136/bmj.m1198 · Cited by 1,653
Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy
Giordano, G., Blanchini, F., Bruno, R., Colaneri, P., Di Filippo, A., Di Matteo, A., & Colaneri, M. (2020) · Nature Medicine · doi:10.1038/s41591-020-0883-7 · Cited by 1,740
COVID-19 and cardiovascular disease
Clerkin, K. J., Fried, J. A., Raikhelkar, J., Sayer, G., Griffin, J. M., Masoumi, A., Jain, S. S., Burkhoff, D., Kumaraiah, D., Rabbani, L., et al. (2020) · Circulation · doi:10.1161/circulationaha.120.046941 · Cited by 1,738
A minimal common outcome measure set for COVID-19 clinical research
Marshall, J. C., Murthy, S., Diaz, J., Adhikari, N. K., Angus, D. C., Arabi, Y. M., Baillie, K., Bauer, M., Berry, S., Blackwood, B., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30483-7 · Cited by 1,641
Metadata
- Authors
- Wu, J. T., Leung, K., Bushman, M., Kishore, N., Niehus, R., de Salazar, P. M., Cowling, B. J., Lipsitch, M., & Leung, G. M.
- Venue
- Nature Medicine
- Published
- 2020-03-19
- Volume
- 26
- Issue
- 4
- Pages
- 506-510
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3011534780
- Cited by
- 1,311
- References
- 5
- FWCI
- 47.83
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Wu, J. T., Leung, K., Bushman, M., Kishore, N., Niehus, R., de Salazar, P. M., Cowling, B. J., Lipsitch, M., & Leung, G. M. (2020). Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nature Medicine. https://doi.org/10.1038/s41591-020-0822-7
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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