Simulating Covid-19 using cellular automata in Singapore

Abstract

With the rapid spread of COVID-19, the infectious disease has continuously caused a sharp increase in human mortality, causing global social panic ever since it was first announced in 2019. In Singapore, COVID-19 has also impacted the economy due to the restrictions that have been put in place to curb the spread of the virus. This shifted the daily lives and careers of countless of people in the population thereby affecting the country’s economy greatly. For ethnic reasons, the spread of disease cannot be experimented on the population to test policy and get the outcome. As such, mathematical models and simulations of such COVID-19 are deemed highly desirable since we can simulate the virus and the impact of policies on the computer without any real-life consequences. The current models of simulation of COVID-19 are typically done with the Susceptible-Infected-Recovered (SIR) model or the Susceptible-Exposed-Infected-Recovered (SEIR) model. These simulations were done during the early stages of the COVID-19 outbreak and some of them are purely only simulations with no comparison with actual real data. Since they were done during the early phases of the outbreak, there was not enough data on COVID-19 collected, as such the simulations could not be verified its correctness. In this thesis, we experimented with a hybrid model of Cellular Automata and SIR model. The simulation will also consider real-life past data to compute its simulations. As for testing, we will verify the simulation’s data against actual data of COVID-19 cases in Singapore.