Modeling efforts for COVID-19 within the US have focused primarily on helping urban centers cope with the consequent health care crisis. The impact of the pandemic on rural communities is still emerging, and these areas have not received the same degree of modeling attention. At the same time, rural communities are different from urban centers in ways that affect the disease and its dynamics: they have lower densities, are more isolated, have smaller social networks, tend to be poorer and older, and have scant health care infrastructure. Rural communities are also the primary source of food production and natural resource extraction in this country. As the pandemic unfolds across the coming months, rural communities will be faced with highly variable circumstances: some will have no infections and be focused on early detection; some will have active cases and be attempting to stop their spread; some will have eliminated active cases and be attempting to reopen economic and community activities while guarding against resurgence. Treating all communities as the same would be foolish. At the local level, decision makers need tools tailored to real communities: tools that emulate the way people come and go and interact there, tools to consider the most relevant interventions, and tools that account for real variation in how able and willing people will be to comply with possible interventions. At the larger health-district and state level, officials need forecasts of how local decisions, health care infrastructure, and the virus itself will interact to drive the epidemic. The purpose of the current proposal is to provide these tools by building a model of COVID-19 for largely rural states that links the dynamics within communities together into a statewide network. This will be achieved in three specific aims. In Aim 1, we develop a predictive epidemiological model of COVID-19 spread and intensity for rural states. This will be done with a spatial, age-structured metapopulation model that relies on differential equations and their stochastic extensions. In Aim 2, we evaluate how potential interventions in individual communities affect outbreak risk, transmission, access to health care, and intervention efficacy and adoption. Here we combine surveys—of both rural and urban communities in Idaho and several broader regions of the US—to estimate patterns of compliance and the motivations behind them. Using these results, we will then use agent-based models of synthetic communities to simulate interventions. Net effects will be relayed up to the statewide model. In Aim 3, we provide support for decision making to state public health officials and local policy makers in rural communities. This will be done by developing two online graphical interfaces for visualizing forecasts and exploring interventions—one high-level application for non-specialists and a second, more sophisticated version, for public health professionals. Education, empowerment, and appr...