A Stochastic Graph-based Model for the Simulation of SARS-CoV-2 Transmission

TL;DR

This paper introduces a comprehensive stochastic graph-based simulation framework for modeling SARS-CoV-2 transmission, integrating spatial, mobility, and epidemiological models to realistically study epidemic dynamics.

Contribution

It presents a novel integrated model combining real city layouts, agent-based mobility, and virus transmission physics for SARS-CoV-2.

Findings

Realistic city graphs derived from Google Maps.

Agent-based mobility simulation using shortest path algorithms.

Incorporation of SARS-CoV-2 transmission parameters.

Abstract

In this work we propose the design principles of a stochastic graph-based model for the simulation of SARS-CoV-2 transmission. The proposed approach incorporates three sub-models, namely, the spatial model, the mobility model, and the propagation model, in order to develop a realistic environment for the study of the properties exhibited by the spread of SARS-CoV-2. The spatial model converts images of real cities taken from Google Maps into undirected weighted graphs that capture the spatial arrangement of the streets utilized next for the mobility of individuals. The mobility model implements a stochastic agent-based approach, developed in order to assign specific routes to individuals moving in the city, through the use of stochastic processes, utilizing the weights of the underlying graph to deploy shortest path algorithms. The propagation model implements both the epidemiological model and the physical substance of the transmission of an airborne virus considering the transmission parameters of SARS-CoV-2. Finally, we integrate these sub-models in order to derive an integrated framework for the study of the epidemic dynamics exhibited through the transmission of SARS-CoV-2.