Simulating the Evolution of Infectious Agents through Human Interaction

Abstract

Human interaction and behavior were proven to be decisive factors for disease spread among the members of a community. Recent events, determined by the COVID-19 outbreak, forced us to develop methods of control, in order to lower the reproductive number and overall, reduce the number of cases. We propose a software simulation method centered on in what manner a virus spreads inside a closed space. This approach required the implementation of a simplified model of human behavior, while considering the factors that influence the rate of transmission between individuals. Agent-based simulation allowed us to identify different control and prevention means, which are efficient in reducing the natural evolution of a pandemic. The results of the analysis offered insight into the rate of spread and human behavior, or habits that led to a significant increase in the total number of infected agents. Recent literature discussed the simulation of disease spread among the inhabitants of larger areas or studied the way that the pathogen can be spread from person to person through droplets containing the virus. Though we strongly consider that previous studies offer important insight, preventing the spread among smaller groups can let us maintain our activity in a safe and supervised manner. The model has been validated by a series of simulations with respect to COVID-19 real data.