Epidemic outcomes following government responses to COVID-19: Insights from nearly 100,000 models.


Journal

Science advances
ISSN: 2375-2548
Titre abrégé: Sci Adv
Pays: United States
ID NLM: 101653440

Informations de publication

Date de publication:
07 Jun 2024
Historique:
medline: 5 6 2024
pubmed: 5 6 2024
entrez: 5 6 2024
Statut: ppublish

Résumé

Government responses to COVID-19 are among the most globally impactful events of the 21st century. The extent to which responses-such as school closures-were associated with changes in COVID-19 outcomes remains unsettled. Multiverse analyses offer a systematic approach to testing a large range of models. We used daily data on 16 government responses in 181 countries in 2020-2021, and 4 outcomes-cases, infections, COVID-19 deaths, and all-cause excess deaths-to construct 99,736 analytic models. Among those, 42% suggest outcomes improved following more stringent responses ("helpful"). No subanalysis (e.g. limited to cases as outcome) demonstrated a preponderance of helpful or unhelpful associations. Among the 14 associations with

Identifiants

pubmed: 38838157
doi: 10.1126/sciadv.adn0671
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

eadn0671

Auteurs

Eran Bendavid (E)

Department of Medicine, Stanford University, Stanford, CA, USA.
Department of Health Policy, Stanford University, Stanford, CA, USA.

Chirag J Patel (CJ)

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

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Classifications MeSH