Evidence map›Paper›PMID 39271786›Full record

ArticleScientific reports2024

Effectiveness of non-pharmaceutical interventions for COVID-19 in USA.

Yuhang Liu, Weihao Wang, Weng-Kee Wong, Wei Zhu

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuhang LiuDepartment of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA.
Weihao WangDepartment of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA.
Weng-Kee WongDepartment of Biostatistics, University of California at Los Angeles, Los Angeles, CA, 90095-1772, USA. wkwong@ucla.edu.
Wei ZhuDepartment of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA. wei.zhu@stonybrook.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Worldwide, governments imposed non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic to contain the pandemic more effectively. We examined the effectiveness of individual NPIs in the United States during the first wave of the pandemic. Three types of analyses were performed. First, a prototypical Bayesian hierarchical model was employed to gauge the effectiveness of five NPIs and they are gathering restriction, restaurant capacity restriction, business closure, school closure, and stay-at-home order in the 42 states with over 100 deaths by the end of the wave. Second, we examined the effectiveness of the face mask mandate, the sixth and most controversial NPI by counterfactual modeling, which is a variant of the prototypical Bayesian hierarchical model allowing us to answer the question of what if the state had imposed the mandate or not. The third analysis used an advanced Bayesian hierarchical model to evaluate the effectiveness of all six NPIs in all 50 states and the District of Columbia, and thereby provide a full-scale estimation of the effectiveness of NPIs and the relative effectiveness of each NPI in the entire United States. Our results have enhanced the collective knowledge on the general effectiveness of NPIs in arresting the spread of COVID-19.

Indexed as

Bayes TheoremCOVID-19MasksCommunicable Disease ControlHumansPandemicsSARS-CoV-2United States

Identifiers

PMID39271786
PMCPMC11399256

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.