Interactive Simulation of Disease Contagion in Dynamic Crowds

Abstract: We propose an agent-to-agent contagion-immunity formulation that can simulate detailed COVID-19 spreading within moving crowds. Specifically, we develop a diffusion-based disease contagion model for discrete systems that considers the effect of health interventions, such as social distancing, immunity, and vaccination. We integrate our contagion-immunity formulation with the governing equations of motion for crowd dynamics for investigating the distribution of disease in crowds with different numbers of people. For the same crowd simulation, our model can interactively simulate virus spread for different initial distributions of infected people. To the best of our knowledge, our work is the first to simulate the disease contagion within moving crowds in computer graphics. Our numerical results for the number of infected people in unprotected dense crowds agree with the SIS model, while our model provides richer information for disease spread and shows that vaccination is the best health intervention to prevent infection.