Fast and exact simulations of stochastic epidemics on static and temporal networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866908398259273728 |
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| author | Cure, Samuel Pflug, Florian G. Pigolotti, Simone |
| author_facet | Cure, Samuel Pflug, Florian G. Pigolotti, Simone |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07095 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast and exact simulations of stochastic epidemics on static and temporal networks Cure, Samuel Pflug, Florian G. Pigolotti, Simone Physics and Society Quantitative Methods 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. |
| title | Fast and exact simulations of stochastic epidemics on static and temporal networks |
| topic | Physics and Society Quantitative Methods |
| url | https://arxiv.org/abs/2412.07095 |