Estimating direct and spill-over impacts of political elections on COVID-19 transmission using synthetic control methods.

Autor: Lim JT; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore., Maung K; Department of Economics, University of Rochester, Rochester, New York, United States of America., Tan ST; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore., Ong SE; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.; Research for Impact, Singapore, Singapore.; The Galen Centre for Health and Social Policy, Kuala Lumpur, Malaysia., Lim JM; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore., Koo JR; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore., Sun H; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore., Park M; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore., Tan KW; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore., Yoong J; Research for Impact, Singapore, Singapore.; Center for Economic and Social Research, University of Southern California, California, United States of America., Cook AR; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore., Dickens BSL; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2021 May 27; Vol. 17 (5), pp. e1008959. Date of Electronic Publication: 2021 May 27 (Print Publication: 2021).
DOI: 10.1371/journal.pcbi.1008959
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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