A modeling study on the critical sensitivity of epidemic peaks and final size to marginal changes in non-pharmaceutical intervention efficacy.

During the COVID-19 pandemic in China, non-pharmaceutical interventions (NPIs) have been changed from the containment to the dynamic zero-case policy (DZCP) to achieve the goal of zero COVID in approximately 40 days, and then to reopening. Epidemic characteristic metrics are similar for multiple outbreaks but with markedly different peak values, timings and final sizes. In order to quantify the sensitivity of epidemic dynamics to the effectiveness of NPIs, and to guide intervention strategies for future outbreaks the data-driven modeling approach is adapted. In particular, to examine to which process or which parameter is sensitive, we used analytic techniques to identify and analyze major changes in epidemiological indices caused by small changes in characteristic metrics. By comparing basic statistics for 80 outbreaks induced by three different strains, we show that the controlled epidemic trajectories of COVID-19 epidemics depend entirely on the efficacy of NPIs: the infectivity of a strain has little relevance, even for the most infectious strains. Thus, minor changes in the strength of NPIs will lead to huge differences in epidemiological indicators such as outbreak peak value and outbreak clearing time.