A stochastic geospatial epidemic model and simulation using an event modulated Gillespie algorithm

TL;DR

This paper presents a stochastic geospatial epidemic model and simulation tool for COVID-19 that incorporates contact networks and geographic data, enabling detailed scenario forecasting and response planning.

Contribution

It introduces a novel model and software package with an optimization framework for quick parameter fitting and geospatial visualization tailored for local epidemic analysis.

Findings

Predicted COVID-19 spread in Geneva using the model.

Demonstrated the tool's ability to fit observed epidemic data.

Provided detailed epidemic scenario visualizations.

Abstract

We developed a model and a software package for stochastic simulations of transmission of COVID-19 and other similar infectious diseases, that takes into account contact network structures and geographical distribution of population density, detailed up to a level of location of individuals. Our analysis framework includes a surrogate model optimization process for quick fitting of the model's parameters to the observed epidemic curves for cases, hospitalizations and deaths. This set of instruments (the model, the simulation code, and the optimizer) is a useful tool for policymakers and epidemic response teams who can use it to forecast epidemic development scenarios in local environments (on the scale from towns to large countries) and design optimal response strategies. The simulation code also includes a geospatial visualization subsystem, presenting detailed views of epidemic scenarios directly on population density maps. We used the developed framework to draw predictions for COVID-19 spreading in the canton of Geneva, Switzerland.