NSF Award
2327862
Rural epidemiology is a critical subfield of epidemiology seeking to examine how the characteristics of rural communities influence health. Although less than one-fifth of the U.S. population lives in rural areas, these areas encompass about 97% of the total land area in the United States and thus the population density for rural areas is low. Traditionally, mainstream epidemiological models focus on settings with high population density, such as major U.S. cities. To study rural epidemiology, it is necessary to develop epidemiological models and analyses tailored to the unique demographics and geographic isolation of rural areas. This grant brings together expertise in mathematical modeling, high-performance computing, geographic information systems, and medical sociology to develop a framework for reliable and accurate real-time epidemic forecasts for mountain-West States, such as Wyoming, Montana, Idaho, and Colorado. The project involves constructing a data-driven multiscale mobility network, using the combination of de-biased mobile phone data, American Community Survey (ACS), and census data in these regions. With a multiscale mobility network, the principal investigators (PIs) will be able to examine how containment policies during a pandemic, such as mobility reduction interventions, stay-at-home orders, and State border lockdowns, impact the transmission dynamics of infectious diseases, and how such policies may exacerbate the disparities of public health and social inequalities in rural areas. The study will help government health officials develop equitable policies and mitigation strategies for rural places in the future epidemic. Furthermore, the proposed community outreach will raise awareness of disease prevention in Wyoming.<br/> <br/>This grant offers solutions to three research questions in rural epidemiology: Q1. How to construct a metapopulation mobility network tailored to rural places? Human mobility is one of the factors driving the spread of infectious diseases across wide geographical ranges. The project introduces a metapopulation model embedded in a multiscale data-driven mobility network tailored to the unique structures of rural population density and human moving patterns for simulating infection dynamics of the front-range States. For this metapopulation mobility model, the PIs will adapt and improve the efficiency of Gillespie’s event-driven algorithms and derive a proper time step for large-scale stochastic simulations. The PIs will also implement an efficient particle filter algorithm for data assimilations to estimate local transmission parameters at two scales. Q2. How to detect and correct mobile phone data bias? If the bias of the mobile phone data used to construct the mobility networks is not addressed, infection dynamics simulations may incorrectly estimate the severity and speed of disease transmission. The project will investigate the bias of mobile phone data used to construct the mobility network and develop a method to offset the data bias. Q3. What are the impacts of containment measures on disadvantaged racial and socioeconomic groups? The PIs will use the proposed unbiased multiscale metapopulation mobility model to study the impacts of temporary mobility reductions and their lasting behavior-modifying effects on the disadvantaged racial and socioeconomic groups in the front-range States.<br/><br/><br/>This project is jointly funded by the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program, the SBE Division of Social and Economic Sciences (SES), and the Established Program to Stimulate Competitive Research (EPSCoR).<br/><br/>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.
