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

Published in PLOS Computational Biology, this study proposes the use of a novel synthetic control framework to obtain causal estimates for direct and spill-over effects of mass gathering on COVID-19 transmission, using the Sabah state elections in Malaysia as an example.

Mass gathering events have been identified as high-risk environments for the community transmission of coronavirus disease 2019 (COVID-19). Empirical estimates of their direct and spill-over effects, however, remain challenging to obtain.

Using the Sabah state elections in Malaysia as an example, this study investigated the event's spatial and temporal impacts on COVID-19 transmission. Results suggest that an estimated 70.0% of COVID-19 case counts within Sabah after the elections were directly attributable to the election. Furthermore, 64.4% of COVID-19 cases in Malaysia during this period were attributable to this election's spill-over effects.

Sensitivity analysis was conducted by examining epidemiological pre-trends, surveillance efforts, varying synthetic control matching characteristics and spill-over specifications. This study confirmed that their estimates are not due to pre-existing epidemiological trends, surveillance efforts, and/or preventive data.

These estimates highlight the potential of mass gatherings in one region to spill-over into an outbreak on a national scale. The relaxations of restrictions on mass gatherings must therefore be carefully considered, even in the context of low community transmission and enforcement of safe distancing measures.

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