Modeling disease spread: Integrating mobility, awareness, and behavior

Abstract The COVID-19 pandemic has underscored the vital link between epidemiology and transportation, particularly in the context of activity-travel behavior. While much research has explored the role of mobility in disease spread, there is a notable gap in understanding how individual behavioral choices, especially regarding testing, influence these dynamics. This paper introduces the mobility-aware behavioral epidemiological model (MABEM), an innovative framework that integrates activity-based modeling with latent variables to more accurately represent the impact of testing decisions on disease propagation. Using data from the canton of Vaud, Switzerland, our model simulates the interactions between individual behavior, mobility, and health status, revealing significant underreporting of infections and highlighting the crucial role of testing choices in shaping observed infection rates. The results suggest that testing behaviors vary significantly across different demographic groups and regions, influencing both individual activity patterns and overall disease spread. MABEM model proves to be computationally efficient and offers valuable insights for designing targeted public health interventions. Despite some limitations related to data availability, this research provides a comprehensive approach to understanding the interplay between mobility, individual behavior, and disease dynamics, offering new possibilities for more effective disease containment strategies.