A Spatial Markov Chain Cellular Automata Model for the Spread of Viruses

TL;DR

This paper introduces a spatial Markov chain cellular automata model to simulate virus spread among humans, considering interpersonal contact intensity and lockdown measures, providing a probabilistic framework for epidemic dynamics.

Contribution

It presents a novel graph-based Markov chain cellular automata model that incorporates lockdown scenarios for virus transmission simulation.

Findings

Model effectively captures virus spread dynamics.

Incorporates lockdown scenarios into the spread simulation.

Provides a probabilistic approach to epidemic modeling.

Abstract

We consider a Spatial Markov Chain model for the spread of viruses. The model is based on the principle to represent a graph connecting nodes, which represent humans. The vertices between the nodes represent relations between humans. In this way, a graph is connected in which the likelihood of infectious spread from person to person is determined by the intensity of interpersonal contact. Infectious transfer is determined by chance. The model is extended to incorporate various lockdown scenarios.