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

Jue Tao Lim, Kenwin Maung, Sok Teng Tan, Suan Ee Ong, Jane Mingjie Lim, Joel Ruihan Koo, Haoyang Sun, Minah Park, Ken Wei Tan, Joanne Yoong, Alex R. Cook, Borame Sue Lee Dickens

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.

Original languageEnglish
Article numbere1008959
JournalPLoS Computational Biology
Volume17
Issue number5
DOIs
StatePublished - May 2021

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