Cell-DEVS Models for the Spread of COVID-19

Improved Susceptible-Infected-Recovered (SIR) models have been used to study the COVID-19 pandemic. Although they can predict epidemiology curves, spatial models cannot be easily built, and cannot model individual interactions. In this research, we show a definition of SIR-based models using the Cell-DEVS formalism (a combination of Cellular Automata and DEVS), showing how to deal with these issues. We validate the equivalence of a simple Cell-DEVS SIR model, and we present a SIIRS model, whose parameters are configured to imitate the spread of SARS-CoV-2 in South Korea. Such models may assist in the decision-making process for defining health policies, such as social distancing, to prevent an uncontrolled expansion of the virus.