Crowd simulation with fear and infection awareness for Ebola transmission

Epidemic dynamics are shaped not only through biological transmission mechanisms but also by psychological factors such as fear and awareness of infection dynamics. In this paper, we propose the Fear and Infection-Awareness Social Force Model (FIA-SFM). This agent-based novel framework dynamically integrates fear and infection awareness dynamics into the Social Force Model (SFM) to simulate actual crowd behaviors during Ebola outbreaks. Unlike conventional models that overlook congestive responses, FIA-SFM introduces a continuous fear quantification system ranging from 0% to 100% which dynamically influences agent movement through velocity modulation and path re-planning. The model visualizes fear dynamics in real-time via Hue, Saturation, Value (HSV) color encoding while incorporating infection repulsion and disease zone avoidance forces. Simulation results demonstrate a significant 78% reduction in Ebola transmission when psychological interventions such as awareness campaigns are factored into the crowd dynamics. Furthermore, the model reveals that populations with heightened awareness exhibit more strategic avoidance behaviors, effectively mitigating disease spread. FIA-SFM provides valuable insights for public health policymakers to simulate and optimize intervention strategies by accounting for both emotional and cognitive behavioral responses during epidemics.