CK22-008 Characterizing dynamics of pandemic and preparing for speedy and accurate response

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Project Summary/Abstract Properties of disease transmission can evolve throughout the pandemic and may be influenced by health policy decisions, regional demographic characteristics, community behaviors, environmental characteristics, and population immunity. This proposal is motivated by significant challenges we have encountered, including dynamic connections between virus evolution, health policy, population behavior and degree of immunity over time, and evolving data elements and data quality due to varying testing criteria and inconsistent reporting behavior. The overarching goal of this proposal is to develop a framework of pandemic predictive intelligence that can adapt over time to changing data quality and evolving behavioral and environmental characteristics that influence disease transmission. The key advantage of the proposed modeling approach is its adaptation to time varying exposures of community behavior and mobility, environmental conditions, mitigation strategies, population immunity, and viral evolution. Through three projects, we will develop models to 1) improve the forecasting accuracy by enhancing model robustness (robustness to data error and model assumptions), 2) connect the dots between viral evolution and transmissibility, and 3) advance the state-of-the-art forecasting by integrating five major components, including viral evolution, transmissibility, social behavior, population immunity and public health policy, to build a learning system for predictive modeling for infectious disease. To ensure the broader impact of the proposed research, we will develop, validate, and evaluate methodology and software for pandemic forecasting, real-time monitoring, mitigation, and prevention of the spread of pathogens using national county/city-level data from the US Department of Health and Human Services, the University of Pennsylvania, and other publicly available data resources. The proposed work will contribute to foundational work needed to advance pandemic science, which includes predictive modelling of pandemic and evidence-assisted health policymaking for pandemic prevention and response.