NSF Award
2421378
This project analyzes mathematical models that incorporate the interaction between human behavior and infection transmission of epidemic diseases. The goal is to inform public health policy decisions that are implemented when a major epidemic is spreading throughout a population. The models encompass social acceptance or resistance to public interventions such as social distancing, public closings, individual isolation, mask wearing, and vaccination. The models simulate evolving disease dynamics and address how different policies affect the control of the epidemic progression. The project advances inter-disciplinary perspectives that facilitate more accurate and applicable models of epidemic diseases. Broader Impacts of this project include a mini-unit on public health integrated in K-12 science and math courses and a bridge program. Mentorship is also an emphasis, notably in a bridge program between Masters and PhD levels to increase diversity in science.<br/><br/><br/>Three classes of models are developed: (1) Agent based models that track individual behavior connected to vaccine hesitancy and public vaccination information; (2) Multi-layered discrete time network models that access the impact of pandemic related cultural shifts and risk perception of disease spread and vaccination acceptance; (3) Compartment differential equations models that incorporate dynamic changes in individual chronological age related human behavior and individual vaccination stages. Data are obtained from the Centers of Disease Control and Prevention, the New York State Department of Health, the National Center for Immunization, and other epidemic data sources.<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.
