RAPID: Identifying Geographic and Demographic Drivers of Rural Disease Transmission for Improved Modeling and Decision Making

This Rapid Response Research (RAPID) grant will improve understanding of drivers of disease transmission in rural areas, providing insights for improved decision-making and public health management in rural communities. The majority of current research relevant to modeling COVID-19 spread is focused on urban systems. Given the vast differences in demographic, social mobility, transportation, and built environment characteristics between rural and urban systems, it is expected that rural spread patterns are different from urban. This project will examine whether this is the case; identify key factors that account for any differences; and how models should be adjusted to better fit rural conditions. Based on those findings, the team will build an epidemiological model well suited to rural communities. The project will also begin the process of evaluating how well risk prevention policies and messages could be adjusted for maximum effectiveness in rural communities. The research team will share relevant findings with county- and state-level public health managers and help them incorporate the findings into best practices.

The goal of this research is to conduct an informed process of spatial, geographic, public health, wastewater infrastructure and social data collection and synthesis for improved pandemic management in rural communities. The project team will examine 3 rural and 3 urban counties in North Carolina. Initial data will be collected and synthesized from COVID-19 epidemiological data at the state and county level, as well as other available published information from COVID-19 research as valuable input to a susceptible-exposed-infected-recovered (SEIR) modeling base approach. From there, a guided process of data collection and synthesis will be used to prioritize factors of importance in disease transmission across rural and urban. Available data sources include health surveillance, cell-phone based mobility, land use features, commuting patterns, essential business proximity, public health infrastructure, and medical care availability. The team will simultaneously gather wastewater samples in selected sewage and package treatment systems across selected counties to quantify the prevalence of SARS-CoV-2 in the relevant locations. This will provide an additional non-clinical metric for disease prevalence. Given current inaccuracies of clinical testing data, particularly in rural areas, these disease-presence data will constitute a key measure of disease presence against which to validate insights emerging from the SEIR model as well as to assess other metrics being used in public health models. The final stages of the project will be to rectify the conceptual framework of disease transmission drivers, and initial SEIR/spatial modeling approaches with NC surveillance system data, and work with county and state level public health managers and epidemiological to incorporate the findings into best practices.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.