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Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19
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 n...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Centers for Disease Control and Prevention
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588530/ https://www.ncbi.nlm.nih.gov/pubmed/32917290 http://dx.doi.org/10.3201/eid2611.201074 |
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author | Biggerstaff, Matthew Cowling, Benjamin J. Cucunubá, Zulma M. Dinh, Linh Ferguson, Neil M. Gao, Huizhi Hill, Verity Imai, Natsuko Johansson, Michael A. Kada, Sarah Morgan, Oliver Pastore y Piontti, Ana Polonsky, Jonathan A. Prasad, Pragati Venkata Quandelacy, Talia M. Rambaut, Andrew Tappero, Jordan W. Vandemaele, Katelijn A. Vespignani, Alessandro Warmbrod, K. Lane Wong, Jessica Y. |
author_facet | Biggerstaff, Matthew Cowling, Benjamin J. Cucunubá, Zulma M. Dinh, Linh Ferguson, Neil M. Gao, Huizhi Hill, Verity Imai, Natsuko Johansson, Michael A. Kada, Sarah Morgan, Oliver Pastore y Piontti, Ana Polonsky, Jonathan A. Prasad, Pragati Venkata Quandelacy, Talia M. Rambaut, Andrew Tappero, Jordan W. Vandemaele, Katelijn A. Vespignani, Alessandro Warmbrod, K. Lane Wong, Jessica Y. |
author_sort | Biggerstaff, Matthew |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7588530 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Centers for Disease Control and Prevention |
record_format | MEDLINE/PubMed |
spelling | pubmed-75885302020-11-01 Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19 Biggerstaff, Matthew Cowling, Benjamin J. Cucunubá, Zulma M. Dinh, Linh Ferguson, Neil M. Gao, Huizhi Hill, Verity Imai, Natsuko Johansson, Michael A. Kada, Sarah Morgan, Oliver Pastore y Piontti, Ana Polonsky, Jonathan A. Prasad, Pragati Venkata Quandelacy, Talia M. Rambaut, Andrew Tappero, Jordan W. Vandemaele, Katelijn A. Vespignani, Alessandro Warmbrod, K. Lane Wong, Jessica Y. Emerg Infect Dis Online Report 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. Centers for Disease Control and Prevention 2020-11 /pmc/articles/PMC7588530/ /pubmed/32917290 http://dx.doi.org/10.3201/eid2611.201074 Text en https://creativecommons.org/licenses/by/4.0/This is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited. |
spellingShingle | Online Report Biggerstaff, Matthew Cowling, Benjamin J. Cucunubá, Zulma M. Dinh, Linh Ferguson, Neil M. Gao, Huizhi Hill, Verity Imai, Natsuko Johansson, Michael A. Kada, Sarah Morgan, Oliver Pastore y Piontti, Ana Polonsky, Jonathan A. Prasad, Pragati Venkata Quandelacy, Talia M. Rambaut, Andrew Tappero, Jordan W. Vandemaele, Katelijn A. Vespignani, Alessandro Warmbrod, K. Lane Wong, Jessica Y. Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19 |
title | Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19 |
title_full | Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19 |
title_fullStr | Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19 |
title_full_unstemmed | Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19 |
title_short | Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19 |
title_sort | early insights from statistical and mathematical modeling of key epidemiologic parameters of covid-19 |
topic | Online Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588530/ https://www.ncbi.nlm.nih.gov/pubmed/32917290 http://dx.doi.org/10.3201/eid2611.201074 |
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