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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.
Read the full article at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008959