Evidence map›Paper›PMID 34077448›Full record

ArticlePloS one2021

Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave.

Nicolas Banholzer, Eva van Weenen, Adrian Lison, Alberto Cenedese, Arne Seeliger, Bernhard Kratzwald, Daniel Tschernutter, Joan Puig Salles, Pierluigi Bottrighi, Sonja Lehtinen and 2 more

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed, 3 pooled it
–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

70 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  4. Age-specific COVID-19 case dynamics during school closure periods: Evidence from 12 european countries.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
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10 more citing papers are in PubMed but not listed here.

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

12 authors.

Nicolas BanholzerDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.ORCID 0000-0003-0138-6120
Eva van WeenenDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
Adrian LisonDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.ORCID 0000-0002-6822-8437
Alberto CenedeseDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
Arne SeeligerDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
Bernhard KratzwaldDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
Daniel TschernutterDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
Joan Puig SallesDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
Pierluigi BottrighiDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
Sonja LehtinenDepartment of Environmental Systems Science, ETH Zurich, Zurich, Switzerland.ORCID 0000-0002-4236-828X
Stefan FeuerriegelDepartment of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.ORCID 0000-0001-7856-8729
Werner VachBasel Academy for Quality and Research in Medicine, Basel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The novel coronavirus (SARS-CoV-2) has rapidly developed into a global epidemic. To control its spread, countries have implemented non-pharmaceutical interventions (NPIs), such as school closures, bans of small gatherings, or even stay-at-home orders. Here we study the effectiveness of seven NPIs in reducing the number of new infections, which was inferred from the reported cases of COVID-19 using a semi-mechanistic Bayesian hierarchical model. Based on data from the first epidemic wave of n = 20 countries (i.e., the United States, Canada, Australia, the EU-15 countries, Norway, and Switzerland), we estimate the relative reduction in the number of new infections attributed to each NPI. Among the NPIs considered, bans of large gatherings were most effective, followed by venue and school closures, whereas stay-at-home orders and work-from-home orders were least effective. With this retrospective cross-country analysis, we provide estimates regarding the effectiveness of different NPIs during the first epidemic wave.

Indexed as

Bayes TheoremCOVID-19Disease OutbreaksEpidemicsHumansPhysical DistancingQuarantineRetrospective StudiesSARS-CoV-2

Identifiers

PMID34077448
PMCPMC8171941

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.