Fast and exact simulations of stochastic epidemics on static and temporal networks

Epidemic models on complex networks have been widely used to study how the social structure of a population affect the spreading of epidemics. However, their numerical simulation can be computationally heavy, especially for large networks. In this paper, we introduce NEXT-Net: a flexible implementat...

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Bibliographic Details
Published in:arXiv.org 2024-12
Main Authors: Cure, Samuel, Pflug, Florian G, Pigolotti, Simone
Format: Article
Language:English
Subjects:
Algorithms
Epidemics
Networks
Numerical models
Online Access:Get full text
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Summary:Epidemic models on complex networks have been widely used to study how the social structure of a population affect the spreading of epidemics. However, their numerical simulation can be computationally heavy, especially for large networks. In this paper, we introduce NEXT-Net: a flexible implementation of the next reaction method for epidemic spreading on both static and temporal networks. By systematic tests on artificial and real-world networks, we find that NEXT-Net is substantially faster than alternative algorithms, while being exact. It permits, in particular, to efficiently simulate epidemics on networks with million of nodes on a standard computer. It is also versatile enough to simulate a broad range of epidemic models of temporal networks, including cases in which the network structure changes in response to the epidemic. Our code is implemented in C++ and accessible from Python and R, thus combining speed with user friendliness. Because of these features, our algorithm constitutes an ideal tool for a broad range of applications.
ISSN:2331-8422