Stochastic modelling of early-stage COVID-19 epidemic dynamics in rural communities in the United States

Abstract

COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected millions of people around the globe. We studied the spread of SARS-CoV-2 across six rural counties in North and South Dakota in the United States. The study period was from early March 2020 to mid-June 2021, during which non-pharmaceutical interventions (NPIs) were in place. The end of the study period coincided with the emergence of the Delta variant in the United States. We modelled the transmission dynamics in each county using a stochastic compartmental model and analysed the data within a Bayesian hierarchical statistical framework. We estimated key epidemiological and surveillance parameters including the reproduction number and reporting probability. We conducted a series of counterfactual analyses in which NPIs were lifted earlier and by varying degrees, modelled as an increase in the transmission rate. Under this range of plausible alternative responses, increases in case counts varied from negligible to substantial, underscoring the importance of timely public health measures and compliance with them. From a methodological perspective, our study demonstrates that despite the inherent high variability in epidemic behaviour in small rural communities, the combination of stochastic modelling and application of Bayesian hierarchical analyses enables the estimation of key epidemiological and surveillance parameters and consideration of the potential impact of alternative public health measures in small low population density communities.