Estimation of the incubation period of COVID-19 using viral load data.


Journal

Epidemics
ISSN: 1878-0067
Titre abrégé: Epidemics
Pays: Netherlands
ID NLM: 101484711

Informations de publication

Date de publication:
06 2021
Historique:
received: 01 08 2020
revised: 18 02 2021
accepted: 12 03 2021
pubmed: 28 3 2021
medline: 16 7 2021
entrez: 27 3 2021
Statut: ppublish

Résumé

The incubation period, or the time from infection to symptom onset, of COVID-19 has usually been estimated by using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in the cases' recall of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used reported data on viral load for 30 hospitalized patients from multiple countries (Singapore, China, Germany, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.85 days (95 % CI: 5.05, 6.77), 2.65 days (2.04, 3.41), and 12.99 days (9.98, 16.79), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach, especially when it is impractical to directly observe the infection event.

Identifiants

pubmed: 33773195
pii: S1755-4365(21)00015-3
doi: 10.1016/j.epidem.2021.100454
pmc: PMC7959696
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

100454

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Keisuke Ejima (K)

Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, IN, USA. Electronic address: kejima@iu.edu.

Kwang Su Kim (KS)

Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan.

Christina Ludema (C)

Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, IN, USA.

Ana I Bento (AI)

Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, IN, USA.

Shoya Iwanami (S)

Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan.

Yasuhisa Fujita (Y)

Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan.

Hirofumi Ohashi (H)

Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan; Department of Applied Biological Science, Tokyo University of Science, Noda, Japan.

Yoshiki Koizumi (Y)

National Center for Global Health and Medicine, Tokyo, Japan.

Koichi Watashi (K)

Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan; Department of Applied Biological Science, Tokyo University of Science, Noda, Japan; MIRAI, JST, Saitama, Japan; Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan.

Kazuyuki Aihara (K)

International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan.

Hiroshi Nishiura (H)

Graduate School of Medicine, Hokkaido University, Hokkaido, Japan.

Shingo Iwami (S)

Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan; MIRAI, JST, Saitama, Japan; Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan; NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan; Science Groove Inc., Fukuoka, Japan. Electronic address: iwamishingo@gmail.com.

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