Studying the Spread of Diseases Using Geographical Data and Irregular Topologies with Cell-DEVS

Abstract

Modeling and Simulation (M&S) techniques have been proven to be effective to understand how diseases spread and assess the effectiveness of decisions aimed to control them (e.g., mobility restrictions). Recently, governments used this approach to determine the evolution of the COVID-19 pandemic. In this context, M&S tools that consider geographical information can improve the quality of the simulations. This research presents a methodology that allows modelers to prototype disease spread models that include geographical information. The model can be easily parameterized for other geographical regions and diseases. We present a case study of a disease spread model to show how this methodology works.