Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
22
07
2021
accepted:
03
08
2022
revised:
12
09
2022
pubmed:
31
8
2022
medline:
15
9
2022
entrez:
30
8
2022
Statut:
epublish
Résumé
Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.
Identifiants
pubmed: 36040963
doi: 10.1371/journal.pcbi.1010465
pii: PCOMPBIOL-D-21-01362
pmc: PMC9467347
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1010465Subventions
Organisme : NIAID NIH HHS
ID : R01 AI137093
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI146555
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Commentaires et corrections
Type : UpdateOf
Déclaration de conflit d'intérêts
I have read the journal’s policy and the authors of this manuscript have the following competing interests: DMW has received consulting fees from Pfizer, Merck, GSK, and Affinivax for topics unrelated to this manuscript and is Principal Investigator on a research grant from Pfizer on an unrelated topic. VEP has received reimbursement from Merck and Pfizer for travel expenses to Scientific Input Engagements unrelated to the topic of this manuscript. JLW has received consulting fees from Revelar Biotherapeutics for topics unrelated to this manuscript. All other authors have declared that no competing interest exist.
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