The unmitigated profile of COVID-19 infectiousness.

COVID-19 SARS-CoV-2 computational biology epidemiology generation interval global health human mitigation systems biology

Journal

eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614

Informations de publication

Date de publication:
01 08 2022
Historique:
received: 31 03 2022
accepted: 27 07 2022
pubmed: 2 8 2022
medline: 24 8 2022
entrez: 1 8 2022
Statut: epublish

Résumé

Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. However, statistical and epidemiological biases could lead to underestimation of the duration of infectiousness. We correct for these biases by curating data from the initial outbreak of the pandemic in China (when mitigation was minimal), and find that the infectiousness profile of the original strain is longer than previously thought. Sensitivity analysis shows our results are robust to model structure, assumed growth rate and potential observational biases. Although unmitigated transmission data is lacking for variants of concern (VOCs), previous analyses suggest that the alpha and delta variants have faster within-host kinetics, which we extrapolate to crude estimates of variant-specific unmitigated generation intervals. Knowing the unmitigated infectiousness profile of infected individuals can inform estimates of the effectiveness of isolation and quarantine measures. The framework presented here can help design better quarantine policies in early stages of future epidemics.

Identifiants

pubmed: 35913120
doi: 10.7554/eLife.79134
pii: 79134
pmc: PMC9391043
doi:
pii:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : CIHR
Pays : Canada

Informations de copyright

© 2022, Sender, Bar-On et al.

Déclaration de conflit d'intérêts

RS, YB, SP, EN, JD, RM No competing interests declared

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Auteurs

Ron Sender (R)

Weizmann Institute of Science, Rehovot, Israel.

Yinon Bar-On (Y)

Weizmann Institute of Science, Rehovot, Israel.

Sang Woo Park (SW)

Department of Ecology and Evolutionary, Princeton University, Princeton, United States.

Elad Noor (E)

Weizmann Institute of Science, Rehovot, Israel.

Jonathan Dushoff (J)

Department of Biology, McMaster University, Hamilton, Canada.
Department of Mathematics and Statistics, McMaster University, Hamilton, Canada.
M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Canada.

Ron Milo (R)

Weizmann Institute of Science, Rehovot, Israel.

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