Integrating the Theory of Planned Behavior and the Health Belief Model into an Agent-based Model of Disease Spread on George Mason University’s Campus
Agent-based models (ABMs) are used to represent certain populations and predict disease outcomes to support policy interventions. However, traditional ABMs of disease spread typically simplify health behavior and provide limited connections to theory and data. Therefore, the goal of this study was to develop a new agent decision-making framework that is informed by both health behavioral theory and data. The decision framework will be integrated with a geospatial ABM that simulates masking behavior and COVID-19 spread on a college campus. Agent decision-making is based on a logistic regression model where the dependent variable is mask use and the independent variables are guided by the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM). Informed by these theories, explanatory variables include the agent’s attitude, subjective norms, perceived behavioral control, perceived severity of COVID-19, perceived susceptibility to COVID-19, perceived benefits to masks, and perceived barriers to masks. The variable odds ratios are informed by collected survey data. The agent decision framework will be integrated into a Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model of disease spread on George Mason University’s campus. We compare two scenarios: disease outcomes where the population does not wear masks and disease outcomes with mask use. Preliminary results found that the peak of the infection curve occurs faster and is larger in the scenario where no masks are used. In the future, we plan to implement the data collected from our survey into the GMU disease spread model.
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