National Science Foundation Development Grant

Glen Nowak is a co-Principal Investigator on a $1 million National Science Foundation Development Grant supported under NSF’s Phase 1 funding for projects involving Predictive Intelligence for Pandemic Prevention. This is an 18-month long multi-investigator, multi-institution project interdisciplinary project to design and evaluate new approaches to infectious disease modeling to better forecast how outbreaks and pandemics will evolve or change, including based on beliefs and intentions regarding public health actions and recommendations. The project is led by University of Georgia Regents’ Professor John Drake of the Odum School of Ecology and includes several faculty members from UGA as well as researchers from the University of Michigan and the Cary Institute of Ecosystem Studies. The researchers will follow an approach pioneered to solve complex engineering problems, collaborating on six demonstration projects that are based upon their core expertise. Nowak and Michael Cacciatore will lead a project that involves designing and implementing a Fall 2022 national survey and then working with infectious disease modelers to incorporate key survey findings into mathematical forecasting models.

Proposal Title: “Improving Infectious Disease Models with Longitudinal Surveys of Health Decision Making Preferences and Influences.”

Abstract: The objective of this project is to create more reliable infectious disease models that are informed by social science regarding health-related preferences, perceptions and intentions/behaviors. This project will design and implement a national longitudinal survey of the US adult population to identify and develop profiles using health decision-making preferences, risk-benefit perceptions, demographics (including political ideology), health information sources used and trusted, preventative behavior intentions/adoption, and willingness to comply with medical countermeasures. We will then create epidemiological models that incorporate demographic segmentation, health decision making preferences, compliance and compliance intentions, and key psychological constructs to assess the effects on epidemiologic dynamics. Finally, this project involves performing computer simulations to identify the survey measures that most affect desired epidemiologic outcomes, and in turn, would be most useful for informing public health policies and guiding outbreak communications.