Project Summary Dr. Eric Lofgren, the PI of this proposal, runs the Lofgren Lab at Washington State University. The goal of this research group is to strengthen the methodological foundation of modeling disease dynamics in small, structured populations, with a focus on how organizations or groups structure themselves. Contact patterns between individuals in small populations arise not only from individual-level decisions, but also from structural constraints such as the staffing levels, work assignments, or the built environment. When aggregated over an entire state or country, the dynamics of these small groups and the structural influences on them can be extremely important to public health. In healthcare environments, these contact patterns can greatly affect patients’ risk of acquiring a healthcare-associated infection. However, the methods and tools to study these populations using mathematical modeling are underdeveloped as compared to those that consider much larger populations. Borrowing from epidemiology and disease ecology, the Lofgren Lab’s research uses stochastic models of disease transmission that incorporate both individual-level variation in how people interact and higher-level contact patterns that are externally imposed, for example, examining how workplace policies or a building’s architecture influences who comes into contact with whom. The proposed five-year research program seeks to strengthen the field’s methods for modeling small, structured populations. The goals of the program are to produce a number of important research outputs. These include not only modeling papers exploring disease dynamics in this population, but also development of computational tools necessary for others to advance this area, including novel synthetic data for methods development. Additionally, the program seeks to develop web-based software platforms that allow clinicians and policymakers to use the methods produced in this program without needing to understand the underlying computational machinery. The proposed research program will also extend the Lab’s work geographically, extending it to rural populations in the United States, Argentina and Eastern Africa. The overall vision of this research program is to employ a four-pronged approach: (i) developing the theoretical basis for particular modeling approaches; (ii) examining phenomena that arise in these populations (e.g., small but intensive outbreaks within healthcare facilities as an early mark of emerging epidemics); (iii) leveraging computational tools to address unique challenges in small populations (e.g., utilizing machine learning and information theory approaches to detect an effect within the extremely noisy data that emerges from these populations); and (iv) producing policy-relevant and actionable information for clinicians and public health decision-makers.