The impact of non-pharmaceutical interventions on early-stage COVID-19 epidemic dynamics in rural communities in the United States

COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected millions of people around the globe. We study the spread of SARS-CoV-2 across six rural counties in North and South Dakota in the United States. The study period is from early March 2020 to mid-June 2021, from near the onset of the pandemic to just before the arrival of the Delta variant in the United States. We model the transmission dynamics in each county using a stochastic compartmental model and analyse the data within a Bayesian hierarchical statistical framework. We estimate the model parameters and characterise the effects of non-pharmaceutical interventions (NPIs) implemented at the time. Counterfactual analyses in which NPIs were lifted earlier indicate that notified cases may have increased by up to 51% in the case of low NPI stringency levels with potential additional deaths. Our study underscores 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 can support the quantitative evaluation of the impact of public health measures in small low population density communities.