Modeling the effects of COVID-19 mobility disruptions on RSV transmission in Seattle, Washington

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSRespiratory Syncytial Virus (RSV) infection is a major cause of acute respiratory hospitalizations in young children and older adults. In early 2020 most countries implemented non-pharmaceutical interventions (NPIs) to slow the spread of SARS-CoV-2. COVID-19 NPIs disrupted the transmission of RSV on a global scale, and many locations did not experience widespread re-circulation until late 2020 or 2021. Here, we use a mechanistic transmission model informed by cellphone mobility data to determine which aspects of population behavior had the greatest influence on post-pandemic RSV rebound in Seattle, Washington. MethodsWe used aggregated mobile device location data to characterize within-city mixing, visitor in-flows, and foot traffic to points of interest in Seattle. We fit an age-structured epidemiological model to data on weekly RSV hospitalizations, allowing for reductions in transmission due to declines in mobility during the pandemic. We compared model fits to observed data to assess which mobility behaviors best capture RSV dynamics during the first two post-pandemic waves in Seattle. ResultsIn Seattle, COVID-19 NPIs perturbed RSV seasonality from 2020 to 2022. Seattle experienced a small out-of-season outbreak in Summer 2021 and an atypically large and early wave in Fall 2022. RSV transmission models incorporating mobility network connectivity (measured as the average shortest path length between Seattle neighborhoods) or the inflow of visitors from outside of Seattle best captured the timing and magnitude of the first two post-pandemic waves. Models including foot traffic to schools or child daycares or within-neighborhood movement produced poor fits to observed data. ConclusionsOur results suggest that case importations from other regions and local spread between neighborhoods had the greatest influence on the timing of RSV reemergence in Seattle. These findings contribute to the understanding of behavioral factors underlying RSV epidemic spread and can inform the timing of preventative measures, such as the administration of immunoprophylaxis.