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Estimating direct and spill-over impacts of political elections on COVID-19 transmission using synthetic control methods
- Source :
- PLoS Computational Biology, Vol 17, Iss 5, p e1008959 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- Mass gathering events have been identified as high-risk environments for community transmission of coronavirus disease 2019 (COVID-19). Empirical estimates of their direct and spill-over effects however remain challenging to identify. In this study, we propose the use of a novel synthetic control framework to obtain causal estimates for direct and spill-over impacts of these events. The Sabah state elections in Malaysia were used as an example for our proposed methodology and we investigate the event’s spatial and temporal impacts on COVID-19 transmission. Results indicate an estimated (i) 70.0% of COVID-19 case counts within Sabah post-state election were attributable to the election’s direct effect; (ii) 64.4% of COVID-19 cases in the rest of Malaysia post-state election were attributable to the election’s spill-over effects. Sensitivity analysis was further conducted by examining epidemiological pre-trends, surveillance efforts, varying synthetic control matching characteristics and spill-over specifications. We demonstrate that our estimates are not due to pre-existing epidemiological trends, surveillance efforts, and/or preventive policies. These estimates highlight the potential of mass gatherings in one region to spill-over into an outbreak of national scale. Relaxations of mass gathering restrictions must therefore be carefully considered, even in the context of low community transmission and enforcement of safe distancing guidelines.<br />Author summary Mass gathering events have been identified as high-risk environments for community transmission of coronavirus disease 2019 (COVID-19). Empirical estimates of their direct and spill-over effects however remain limited. We frame the Sabah state election in Malaysia as a natural experiment to investigate the event’s spatial and temporal impacts on COVID-19 transmission. We estimate the direct and spill-over impact of the elections through a synthetic control methodology. Results indicate an estimated (i) 70.0% of COVID-19 case counts within Sabah post-state election were attributable to the election’s direct effect; (ii) 64.4% of COVID-19 cases in the rest of Malaysia post-state election were attributable to the election’s spill-over effects. We demonstrate that our estimates are not due to pre-existing epidemiological trends, surveillance efforts, and/or preventive policies. These estimates highlight the potential of mass gatherings in one region to spill-over into an outbreak of national scale. Relaxations of mass gathering restrictions must therefore be carefully considered, even in the context of low community transmission and enforcement of safe distancing guidelines.
- Subjects :
- Viral Diseases
Epidemiology
0211 other engineering and technologies
Social Sciences
Artificial Gene Amplification and Extension
Transportation
02 engineering and technology
Elections
Polymerase Chain Reaction
01 natural sciences
Disease Outbreaks
law.invention
Geographical Locations
010104 statistics & probability
Medical Conditions
law
Mass gathering
Medicine and Health Sciences
Econometrics
Biology (General)
Enforcement
Virus Testing
Disease surveillance
Ecology
Politics
Transportation Infrastructure
Infectious Diseases
Geography
Transmission (mechanics)
Computational Theory and Mathematics
Modeling and Simulation
Scale (social sciences)
Engineering and Technology
Research Article
Matching (statistics)
Asia
Airports
QH301-705.5
Political Science
Context (language use)
Research and Analysis Methods
Civil Engineering
Cellular and Molecular Neuroscience
Diagnostic Medicine
Genetics
Humans
0101 mathematics
Molecular Biology Techniques
Molecular Biology
Ecology, Evolution, Behavior and Systematics
021110 strategic, defence & security studies
SARS-CoV-2
Malaysia
COVID-19
Biology and Life Sciences
Covid 19
Models, Theoretical
Crowding
Medical Risk Factors
People and Places
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....30039260b2b5826252c483387e83ede9
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1008959