PROJECT SUMMARY Effective modeling of epidemic transmission depends on both a mathematical description of the most important aspects of disease transmission and progression, and estimation of parameters from data. Collection of genetic sequences of SARS-CoV-2 samples throughout the COVID-19 pandemic has produced an extensive new data source to supplement more traditional public health ones. The geographical population structure in Alaska, characterized by subpopulations with well-defined interconnections, offers a unique opportunity to use such data to develop new models and parameter estimation methods. Moreover, in addition to geographic heterogeneity, the progression of pandemic has involved changes in prevalence of coronavirus variants as well as mitigation strategies, so that parameters have a temporal dependence as well. This project leverages public health and sequencing data from the coronavirus pandemic through three main modeling thrusts. A statistical model of variant spread, which captures both geographic structure and changing variant proportions will be developed and applied to the Alaskan genomic data set. Estimation of transmission rates in and between regions using phylodynamic approaches will be investigated. This includes development of a new statistical approach for rapid estimation of parameters that change through time, as well as a focused effort to study transmission patterns between subpopulations. Finally, a traditional compartment model of COVID-19 in Alaska will be developed, which captures the key geographic features affecting disease dynamics in the state. While initially fitted using public health data such as hospital incidence rates and testing results, it will then be modified to better reflect insights and estimates emerging from the first two thrusts. A goal is to promote and demonstrate improved predictive ability from incorporation of sequencing data into the modeling pipeline. Although the project focuses on modeling and data from Alaska, its methods and conclusions will be more broadly applicable. Areas exhibiting similar population structure of an urban/suburban core and smaller, largely rural regions with well-defined interconnections, are common in less developed countries, but they may lack sufficient data for effective modeling. Insights into disease dynamics arising from this structure will also improve understanding of aspects of epidemic progression in regions with more diffuse interconnections.