CAsimulations: Modeling of topological dynamics in a disease using cellular automata

TL;DR

This paper introduces cellular automata models to analyze disease spread influenced by social interactions, providing a new tool and methodology for epidemiological modeling based on topological and dynamical principles.

Contribution

It presents a novel approach to modeling epidemiological phenomena using cellular automata with topological and dynamical variations, enabling better analysis of social interaction impacts.

Findings

Models capture social interaction effects on disease spread

Methodology for constructing epidemiological models from logical rules

Variations demonstrate different topological and dynamical behaviors

Abstract

The prediction of the behavior of the disease, the level of affectation in a population and the ways to control it are the most important aspects studied by epidemiology using tools such as historical data and mathematical models. So, our objective is (1) to provide a tool capable of analyzing epidemiological phenomena starting from the most common social interactions within a group of individuals. (2) To provide a methodology to build epidemiological models from patterns and logical rules. (3) Determine the impact of social interactions on the spread of a disease. This paper describes the logical construction of two epidemiological models in cellular automata together with two of their variations based on topological and dynamical principles.