The Impact of Mobility and Interventions on the Spread of Diseases

Human behavior plays a vital role in spreading contagious diseases, and understanding its influence on the outbreak could be the key to devising better mitigatory strategies. The varied individual behaviors give rise to different patterns in the evolution of the disease. Such trends in the disease outbreak that arise due to microscopic behaviors of heterogeneous agents in a populace are typical of agent-based models. Therefore, we propose an agent-based framework equipped with a population mixing algorithm, and stochastic disease transmission and evolutionary dynamics. The mixing algorithm incorporates different mobility trends in a synthetic populace to generate contact patterns among them. On the other hand, the disease model characterizes the health condition of agents using three crucial traits of the disease: 1) infection status; 2) severity; and 3) awareness, endowed with age-dependent probabilities for transmission, progress, and recovery of the disease. Together with different mobility patterns, we demonstrate the impact of mitigatory measures, such as social distancing, vaccination, testing, isolation, and containment zones, on the spread of disease. The proposed model is used to demonstrate trends in the COVID-19 outbreak rather than predict its course of evolution. The results indicate that enhanced mobility of agents facilitates contagion, and a combination of mitigation strategies needs to be adopted to combat the spread of the disease effectively.