Efficient Stochastic Simulation of Network Topology Effects on the Peak Number of Infections in Epidemic Outbreaks

TL;DR

This paper presents an efficient stochastic simulation method using a CTMC model and Gillespie's SSA to analyze how contact network structures influence the peak number of infections during epidemics, with applications to COVID-19 data.

Contribution

It introduces a realistic, efficient simulation approach for moderate-sized networks that avoids mean-field assumptions and is applicable to real epidemic data.

Findings

Identifies network topology features affecting infection peaks

Provides a Python-based simulation tool with an interactive dashboard

Analyzes COVID-19 and demographic data from Ukraine

Abstract

This paper investigates the effect of the structure of the contact network on the dynamics of the epidemic outbreak. In particular, we focus on the peak number of critically infected nodes (PCIN), determining the maximum workload of intensive healthcare units which should be kept low. As a model and simulation method, we develop a continuous-time Markov chain (CTMC) model and an efficient simulation-based on Gillespie's Stochastic Simulation Algorithm (SSA). This methods combine a realistic approximation of the stochastic process not relying on the assumptions of mean-field models and large asymptotically large population sizes as in differential equation models, and at the same time an efficient way to simulate networks of moderate size. The approach is analysed for different scenarios, based on data from the COVID-19 outbreak and demographic data from Ukraine. From these results we extract network topology features that need to be considered to effectively decrease the peak number of infections. The CTMC simulation is implemented in python and integrated in a dashboard that can be used for interactive exploration and it is made openly available.