Ranking the effectiveness of worldwide COVID-19 government interventions
Open accessPublished version of a preprint
Abstract
Assessing the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate the spread of SARS-CoV-2 is critical to inform future preparedness response plans. Here we quantify the impact of 6,068 hierarchically coded NPIs implemented in 79 territories on the effective reproduction number, R t, of COVID-19. We propose a modelling approach that combines four computational techniques merging statistical, inference and artificial intelligence tools. We validate our findings with two external datasets recording 42,151 additional NPIs from 226 countries. Our results indicate that a suitable combination of NPIs is necessary to curb the spread of the virus. Less disruptive and costly NPIs can be as effective as more intrusive, drastic, ones (for example, a national lockdown). Using country-specific 'what-if' scenarios, we assess how the effectiveness of NPIs depends on the local context such as timing of their adoption, opening the way for forecasting the effectiveness of future interventions.
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Beyond the economic costs, lockdowns have also been shown to have adverse corollaries in terms of non-Covid health outcomes, disruption to education and increase in domestic violence.
“Beyond the economic costs, lockdowns have also been shown to have adverse corollaries in terms of non‐Covid health outcomes, disruption to education and increase in domestic violence (Haug et al., 2020).”
From Living through lockdown: A qualitative exploration of individuals' experiences in the UK. (2022) · cites this paper as Haug et al., 2020
Although lockdowns have been shown to save lives, both their individual and collective cost is not yet understood.
“Although lockdowns have been shown to save lives, both their individual and collective cost is not yet understood.”
From Living through lockdown: A qualitative exploration of individuals' experiences in the UK. (2022) · cites this paper as Haug et al., 2020
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References 64 · Cited by 1,412
- Authors
- Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V., Pinior, B., Thurner, S., & Klimek, P.
- Affiliations
- Complexity Science Hub (AT)
- University of Veterinary Medicine Vienna (AT)
- Corresponding author
- Klimek, Peter
- ORCID
- Venue
- Nature Human Behaviour
- Published
- 2020-11-16
- Volume
- 4
- Issue
- 12
- Pages
- 1303-1312
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W3106386073
- Cited by
- 1,412
- References
- 64
- Citations by year
- 2026: 64 · 2025: 113 · 2024: 166 · 2023: 254 · 2022: 416 · 2021: 370 · 2020: 28 · 2012: 1
- FWCI
- 40.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Vienna Science and Technology Fund (MA16-045, 20-001, COV 20-017)
- Österreichische Forschungsförderungsgesellschaft (857136)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- COVID-19 pandemic, SARS-CoV-2, non-pharmaceutical interventions, state intervention, effective reproduction number, lockdown, risk communication, health system capacity, intervention timing, what-if scenarios, pandemic preparedness
- MeSH terms
- COVID-19, Artificial Intelligence, Government, Humans, Models, Theoretical, Global Health, Basic Reproduction Number, Datasets as Topic
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