Modeling Coupled Disease-Behavior Dynamics of SARS-CoV-2 Using Influence Networks

SARS-CoV-2, the virus that causes COVID-19, has caused significant human morbidity and mortality since its emergence in late 2019. Not only have over three million people died, but humans have been forced to change their behavior in a variety of ways, including limiting their contacts, social distancing, and wearing masks. Early infectious disease models, like the classical SIR model by Kermack and McKendrick, do not account for differing contact structures and behavior. More recent work has demonstrated that contact structures and behavior can considerably impact disease dynamics. We construct a coupled disease-behavior dynamical model for SARS-CoV-2 by incorporating heterogeneous contact structures and decisions about masking. We use a contact network with household, work, and friend interactions to capture the variation in contact patterns. We allow decisions about masking to occur at a different time scale from disease spread which dramatically changes the masking dynamics. Drawing from the field of game theory, we construct an individual decision-making process that relies on perceived risk of infection, social influence, and individual resistance to masking. Through simulation, we find that social influence prevents masking, while perceived risk largely drives individuals to mask. Underlying contact structure also affects the number of people who mask. This model serves as a starting point for future work which could explore the relative importance of social influence and perceived risk in human decision-making.