No abstract

The novel coronavirus SARS-CoV-2 emerged in 2019 and subsequently spread throughout the world, causing over 529 million cases and 6 million deaths thus far. In this study, we formulate a continuous-time Markov chain model to investigate the influence of superspreading events (SSEs), defined here as public or social events that result in multiple infections over a short time span, on SARS-CoV-2 outbreak dynamics. Using Gillespie's direct algorithm, we simulate a continuous-time Markov chain model for SARS-CoV-2 spread under multiple scenarios: first, with neither hospitalisation nor quarantine; second, with hospitalisation, quarantine, premature hospital discharge, and quarantine violation; and third, with hospitalisation and quarantine but neither premature hospital discharge nor quarantine violation. We also vary quarantine violation rates. Results indicate that, in most cases, SSE-dominated outbreaks are more variable but less severe than non-SSE-dominated outbreaks, though the most severe SSE-dominated outbreaks are more severe than the most severe non-SSE-dominated outbreaks. SSE-dominated outbreaks are outbreaks with relatively higher SSE rates. In all cases, SSE-dominated outbreaks are more sensitive to control measures, with premature hospital discharge and quarantine violation substantially reducing control measure effectiveness.