RAISE: IHBEM: Mathematical Formulations of Human Behavior Change in Epidemic Models

This research contributes to epidemiological modeling by integrating mathematical modeling and social and behavioral sciences to enhance the infectious disease modeling paradigm. From social distancing and mask-wearing in response to perceived risk of infection to changes in non-pharmaceutical Interventions under economic pressures, human responses altered the COVID-19 pandemic outcomes. In this project, foundational steps are taken toward the vision of merging behavioral and epidemiological models. This line of research leads to epidemiological models that represent human behavior and the spread of the disease in interconnected structures, contribute to more accurate forecasting of an epidemic, and enhance policy-making with major impacts on societal well-being. This project is funded jointly by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral, and Economic Sciences (SBE). The three specific objectives of this research are: (i) Model human behavior with a focus on five behavioral constructs: interactions (e.g., mobility), compliance with preventive measures (e.g., mask use), willingness to vaccinate, risk perception, and adherence fatigue, all of which are at the nexus connecting personal decision or government policies to outbreak dynamics; (ii) Integrate these human behavior mechanisms into disease models so that they are projected endogenously; and (iii) Analyze the resulting changes in epidemic forecasting as well as their implications on model-based policy recommendations regarding vaccine prioritization, economic-public health tradeoffs, and emergence of a new normal endemic state. Mathematically, the system dynamics approach builds on ordinary differential equation models and various statistical methods for estimation and validation. The modeling approach and validation techniques rely on multiple data sources on human behavior and the spread of the disease during the COVID-19 pandemic. Upon building and validating coupled behavior-disease models, the central hypothesis of the study is tested: Models that incorporate human behavioral changes endogenously outperform those that lack such feedback mechanisms in long-term forecasting tasks, and offer distinct policy recommendations. 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.