Estimating SARS-CoV-2 transmission parameters between coinciding outbreaks in a university population and the surrounding community

Abstract

AbstractPrior studies suggest that population heterogeneity in SARS-CoV-2 (COVID-19) transmission plays an important role in epidemic dynamics. During the fall of 2020, many US universities and the surrounding communities experienced an increase in reported incidence of SARS-CoV-2 infections, with a high disease burden among students. We explore the transmission dynamics of an outbreak of SARS-CoV-2 among university students, how it impacted the non-student population via cross-transmission, and how it could influence pandemic planning and response.Using surveillance data of reported SARS-CoV-2 cases, we developed a two-population SEIR model to estimate transmission parameters and evaluate how these subpopulations interacted during the 2020 Fall semester. We estimated the transmission rate among the university students (βU) and community residents (βC), as well as the rate of cross-transmission between the two subpopulations (βM) using particle Markov Chain Monte Carlo (pMCMC) simulation-based methods.We found that both populations were more likely to interact with others in their population and that cross-transmission was minimal. The cross-transmission estimate (βM) was considerably smaller [0.04 x 10-5(95% CI: 0.00 x 10-5, 0.15 x 10-5)] compared to the community estimate (βC) at 2.09 x 10-5(95% CI: 1.12 x 10-5, 2.90 x 10-5) and university estimate (βU) at 27.92 x 10-5(95% CI: 19.97 x 10-5, 39.15 x 10-5). The higher within population transmission rates among the university and the community (698 and 52 times higher, respectively) when compared to the cross-transmission rate, suggests that these two populations did not transmit between each other heavily, despite their geographic overlap.During the first wave of the pandemic, two distinct epidemics occurred among two subpopulations within a relatively small US county population where university students accounted for roughly 41% of the total population. Transmission parameter estimates varied substantially with minimal or no cross-transmission between the subpopulations. Assumptions that county-level and other small populations are well-mixed during a respiratory viral pandemic should be reconsidered. More granular models reflecting overlapping subpopulations may assist with better-targeted interventions for local public health and healthcare facilities.