A simple model of coupled individual behavior and its impact on epidemic dynamics

Containing infectious disease outbreaks is a complex challenge that usually requires the deployment of multiple intervention strategies. While mathematical modeling of infectious diseases is a widely accepted tool to evaluate intervention strategies, most models and studies overlook the interdependence between individuals' reactions to simultaneously implemented interventions. Intervention modeling efforts typically assume that individual adherence decisions are independent of each other. However, in the real world, individuals who are willing to comply with certain interventions may be more or less likely to comply with another intervention. The combined effect of interventions may depend on the correlation between adherence decisions. In this study, we consider vaccination and non-pharmaceutical interventions, and study how the correlation between individuals' behaviors towards these two interventions strategies affects the epidemiological outcomes. Furthermore, we integrate disease surveillance in our model to study the effects of interventions triggered by surveillance events. This allows us to model a realistic operational context where surveillance informs the timing of interventions deployment, thereby influencing disease dynamics. Our results demonstrate the diverse effects of coupled individual behavior and highlight the importance of robust surveillance systems. Our study yields the following insights: (i) there exists a correlation level that minimizes the initial prevalence peak size; (ii) the optimal correlation level depends on the disease's basic reproduction number; (iii) disease surveillance modulates the impact of interventions on reducing the epidemic burden.