Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1008959
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dc.titleEstimating direct and spill-over impacts of political elections on COVID-19 transmission using synthetic control methods
dc.contributor.authorLim, Jue Tao
dc.contributor.authorMaung, Kenwin
dc.contributor.authorTan, Sok Teng
dc.contributor.authorOng, Suan Ee
dc.contributor.authorLim, Jane Mingjie
dc.contributor.authorKoo, Joel Ruihan
dc.contributor.authorSun, Haoyang
dc.contributor.authorPark, Minah
dc.contributor.authorTan, Ken Wei
dc.contributor.authorYoong, Joanne
dc.contributor.authorCook, Alex R.
dc.contributor.authorDickens, Borame Sue Lee
dc.date.accessioned2022-10-12T08:06:55Z
dc.date.available2022-10-12T08:06:55Z
dc.date.issued2021-05-27
dc.identifier.citationLim, Jue Tao, Maung, Kenwin, Tan, Sok Teng, Ong, Suan Ee, Lim, Jane Mingjie, Koo, Joel Ruihan, Sun, Haoyang, Park, Minah, Tan, Ken Wei, Yoong, Joanne, Cook, Alex R., Dickens, Borame Sue Lee (2021-05-27). Estimating direct and spill-over impacts of political elections on COVID-19 transmission using synthetic control methods. PLoS Computational Biology 17 (5) : e1008959. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1008959
dc.identifier.issn1553-734X
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232473
dc.description.abstractMass 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. © 2021 Lim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.publisherPublic Library of Science
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.typeArticle
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.contributor.departmentDEAN'S OFFICE (SSH SCH OF PUBLIC HEALTH)
dc.contributor.departmentDEAN'S OFFICE (MEDICINE)
dc.description.doi10.1371/journal.pcbi.1008959
dc.description.sourcetitlePLoS Computational Biology
dc.description.volume17
dc.description.issue5
dc.description.pagee1008959
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