Evidence map›Paper›PMID 38838157›Full record

ArticleScience advances2024

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

Eran Bendavid, Chirag J Patel

Erratum issuedAbstract read
In one paragraph

Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
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  6. Estimating the extent and sources of model uncertainty in political science.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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  8. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Eran BendavidDepartment of Medicine, Stanford University, Stanford, CA, USA.ORCID 0000-0002-8364-4711
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-8756-8525

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

COVID-19GovernmentSARS-CoV-2HumansModels, TheoreticalPandemics

Identifiers

PMID38838157
PMCPMC11152132

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.