Analytical and cellular automaton approach to a generalized SEIR model for infection spread in an open crowded space

TL;DR

This paper introduces a generalized SEIR model on graphs for infection spread in crowded spaces, combining analytical and cellular automaton methods to quantify how behavioral interventions can reduce disease transmission.

Contribution

It develops a novel graph-based SEIR model and integrates cellular automaton simulations to analyze infection dynamics in open crowded environments.

Findings

Infection ratio depends on controllable parameters.

Behavioral interventions can significantly reduce disease spread.

Model applicable to various topologies and crowd configurations.

Abstract

We formulate a generalized susceptible exposed infectious recovered (SEIR) model on a graph, describing the population dynamics of an open crowded place with an arbitrary topology. As a sample calculation, we discuss three simple cases, both analytically, and numerically, by means of a cellular automata simulation of the individual dynamics in the system. As a result, we provide the infection ratio in the system as a function of controllable parameters, which allows for quantifying how acting on the human behavior may effectively lower the disease spread throughout the system.