Estimating effective reproduction number using generation time versus serial interval, with application to covid-19 in the Greater Toronto Area, Canada.
COVID-19
Generation time
Incubation period
Reproduction number
Serial interval
Journal
Infectious Disease Modelling
ISSN: 2468-0427
Titre abrégé: Infect Dis Model
Pays: China
ID NLM: 101692406
Informations de publication
Date de publication:
2020
2020
Historique:
received:
26
05
2020
accepted:
21
10
2020
entrez:
9
11
2020
pubmed:
10
11
2020
medline:
10
11
2020
Statut:
ppublish
Résumé
The effective reproduction number We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of We estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of Approximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating
Sections du résumé
BACKGROUND
BACKGROUND
The effective reproduction number
METHODS
METHODS
We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of
RESULTS
RESULTS
We estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of
IMPLICATIONS
CONCLUSIONS
Approximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating
Identifiants
pubmed: 33163739
doi: 10.1016/j.idm.2020.10.009
pii: S2468-0427(20)30063-4
pmc: PMC7604055
doi:
Types de publication
Journal Article
Langues
eng
Pagination
889-896Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2020 The Authors.
Déclaration de conflit d'intérêts
None.
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