Computer modeling and simulation to predict COVID-19 propagation patterns via factual cellular automata

Abstract

Computer modelling and simulation methods are very important and play a critical role in the mitigation and response to the ongoing COVID-19 pandemic. In this study, we propose a computational modeling technique based on Cellular Automata (CA) with realistic proposed rules. The rules are designed to simulate the propagation of COVID-19 disease through a bounded area. Our proposed CA rules are novel in many respects. For on, the classification of neighbors to nearest neighbors and range of neighbors based on cellular layers is explained. Moreover, the concepts of time generation and access time are deployed for the first time to model the propagation of the disease over time in this work. Further details of the proposed model including the topology of the defined area, the initial states of the cells and four-layer transfer mechanism are explained as well. This work may be considered a criterion of spreading for COVID-19 from point source in a defined population area. The results of the proposed algorithm represent the percentage of the population whose infectious status is described by different cellular state objects after a defined generation time. The results are compared under different circumstances and analyzed equanimity.