Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19
| Autor: | Matthew Biggerstaff, Benjamin J. Cowling, Zulma M. Cucunubá, Linh Dinh, Neil M. Ferguson, Huizhi Gao, Verity Hill, Natsuko Imai, Michael A. Johansson, Sarah Kada, Oliver Morgan, Ana Pastore y Piontti, Jonathan A. Polonsky, Pragati Venkata Prasad, Talia M. Quandelacy, Andrew Rambaut, Jordan W. Tappero, Katelijn A. Vandemaele, Alessandro Vespignani, K. Lane Warmbrod, Jessica Y. Wong |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Emerging Infectious Diseases, Vol 26, Iss 11, Pp - (2020) |
| Druh dokumentu: | article |
| ISSN: | 1080-6040 1080-6059 |
| DOI: | 10.3201/eid2611.201074 |
| Popis: | We report key epidemiologic parameter estimates for coronavirus disease identified in peer-reviewed publications, preprint articles, and online reports. Range estimates for incubation period were 1.8–6.9 days, serial interval 4.0–7.5 days, and doubling time 2.3–7.4 days. The effective reproductive number varied widely, with reductions attributable to interventions. Case burden and infection fatality ratios increased with patient age. Implementation of combined interventions could reduce cases and delay epidemic peak up to 1 month. These parameters for transmission, disease severity, and intervention effectiveness are critical for guiding policy decisions. Estimates will likely change as new information becomes available. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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