Cellular automata in the light of COVID-19

TL;DR

This paper uses a cellular automata model to analyze the spatial and temporal spread of COVID-19, incorporating social confinement and infectivity parameters, and fits the model to India's data across different waves.

Contribution

It introduces a novel CA-based framework with specific parameters to model COVID-19 spread and social restrictions, validated against real-world data.

Findings

Model effectively captures COVID-19 spread dynamics.

Parameters vary across different waves, reflecting changes in infectivity and social restrictions.

Spatial growth patterns align with observed data.

Abstract

Currently, the world has been facing the brunt of a pandemic due to a disease called COVID-19 for the last 2 years. To study the spread of such infectious diseases it is important to not only understand their temporal evolution but also the spatial evolution. In this work, the spread of this disease has been studied with a cellular automata (CA) model to find the temporal and the spatial behavior of it. Here, we have proposed a neighborhood criteria which will help us to measure the social confinement at the time of the disease spread. The two main parameters of our model are (i) disease transmission probability (q) which helps us to measure the infectivity of a disease and (ii) exponent (n) which helps us to measure the degree of the social confinement. Here, we have studied various spatial growths of the disease by simulating this CA model. Finally we have tried to fit our model with the COVID-19 data of India for various waves and have attempted to match our model predictions with regards to each wave to see how the different parameters vary with respect to infectivity and restrictions in social interaction.