Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19
Open accessAbstract
OBJECTIVES: To evaluate which non-pharmaceutical interventions (NPIs) have been more and less effective in controlling the COVID-19 pandemic. METHODS: We performed a systematic review of published and unpublished empirical studies, either observational or interventional, analysing the comparative effectiveness of NPIs against the COVID-19 pandemic. We searched Embase/Medline and medRxiv to identify the relevant literature. RESULTS: We identified 34 studies. During the first wave of the COVID-19 pandemic, school closing was the most effective NPI, followed by workplace closing, business and venue closing and public event bans. Public information campaigns and mask wearing requirements were also effective in controlling the pandemic while being less disruptive for the population than other NPIs. There was no evidence on the effectiveness of public transport closure, testing and contact tracing strategies and quarantining or isolation of individuals. Early implementation was associated with a higher effectiveness in reducing COVID-19 cases and deaths, while general stringency of the NPIs was not. CONCLUSIONS: In this systematic review, we found that school closing, followed by workplace closing, business and venue closing and public event bans were the most effective NPIs in controlling the spread of COVID-19. An early response and a combination of specific social distancing measures are effective at reducing COVID-19 cases and deaths. Continuous monitoring of NPIs effectiveness is needed in order to adapt decision making.
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Implementing public health and social measures early-such as closing businesses and venues, banning public events, running public information campaigns, and requiring masks-keeps cumulative cases and deaths lower than delaying them.
“Early implementation of public health and social measures—including closing businesses and venues, banning public events, launching public information campaigns, and requiring the use of face masks—is more effective at keeping cumulative cases and deaths low than implementation at a later stage.27”
From The Lancet Commission on lessons for the future from the COVID-19 pandemic (2022) · cites this paper as [27]
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Flagged first, then by citations
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Sorted by citations
The Lancet Commission on lessons for the future from the COVID-19 pandemic
Sachs, J. D., Karim, S. S. A., Aknin, L., Allen, J., Brosbøl, K., Colombo, F., Barron, G. C., Espinosa, M. F., Gaspar, V., Gaviria, A., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01585-9 · Cited by 842 · see citing works
- Authors
- Mendez-Brito, A., El Bcheraoui, C., & Pozo-Martin, F.
- Affiliations
- Robert Koch Institute (DE)
- Corresponding author
- Pozo-Martin, Francisco
- Venue
- Journal of Infection
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 83
- Issue
- 3
- Pages
- 281-293
- ISSN
- 0163-4453 (ISSN-L)
- PMC
- PMC8214911
- OpenAlex
- W3175958360
- Cited by
- 269
- References
- 112
- Citations by year
- 2026: 22 · 2025: 32 · 2024: 68 · 2023: 64 · 2022: 62 · 2021: 21
- FWCI
- 5.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Infection (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Publication server of the Robert Koch Institute (Robert Kock Institute) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
- Physical Sciences › Environmental Science › Global and Planetary Change › COVID-19 impact on air quality
- Keywords
- COVID-19 pandemic, non-pharmaceutical interventions, school closures, workplace closures, information campaigns, social distancing, early intervention
- MeSH terms
- Physical Distancing, COVID-19, Humans, Masks, Quarantine, Contact Tracing, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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