Modeling Epidemics on Multiplex Networks: Epidemic Threshold and Basic Reproduction Number
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915554558738432 |
|---|---|
| author | Rozan, Eric Alejandro Simoy, Mario Ignacio Bouzat, Sebastian Kuperman, Marcelo Nestor |
| author_facet | Rozan, Eric Alejandro Simoy, Mario Ignacio Bouzat, Sebastian Kuperman, Marcelo Nestor |
| contents | Accurate epidemic forecasting requires models that account for the layered and heterogeneous nature of real social interactions. The basic reproduction number $\mathcal R_0$ calculated from models that assume homogeneous mixing or single-layer contact structures have limited applicability to complex social systems. Here, we propose an expression of $\mathcal R_0$ in the context of multiplex networks, enabling the analysis of disease transmission across multiple social layers.
We adapt the Degree-Based Mean-Field (DBMF) SIR model for single-layered complex networks to the multiplex setting, where each layer has its own degree distribution and infection rate. Using the Next Generation Matrix method, we derive an analytical expression for the basic reproduction number $\mathcal R_0$. Numerical integration of the multiplex DBMF equations shows that $\mathcal R_0 = 1$ marks the epidemic threshold and governs the functional dependence of key outbreak indicators. In addition to the exact result for the $\mathcal R_0$, we provide an approximation denoted as $τ$, which is easier to compute and more straightforward to interpret in terms of the parameters of the system, and shares most of the expected properties of the basic reproduction number.
Stochastic agent-based simulations confirm these results, demonstrating a direct correspondence between $τ$ and the average number of secondary infections in the early epidemic phase, in line with the interpretation of $\mathcal R_0$.
This research provides a robust generalization of $\mathcal R_0$ for layered contact structures, offering a more realistic basis for epidemic forecasting and the design of intervention strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12614 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Epidemics on Multiplex Networks: Epidemic Threshold and Basic Reproduction Number Rozan, Eric Alejandro Simoy, Mario Ignacio Bouzat, Sebastian Kuperman, Marcelo Nestor Physics and Society Statistical Mechanics Adaptation and Self-Organizing Systems Populations and Evolution Accurate epidemic forecasting requires models that account for the layered and heterogeneous nature of real social interactions. The basic reproduction number $\mathcal R_0$ calculated from models that assume homogeneous mixing or single-layer contact structures have limited applicability to complex social systems. Here, we propose an expression of $\mathcal R_0$ in the context of multiplex networks, enabling the analysis of disease transmission across multiple social layers. We adapt the Degree-Based Mean-Field (DBMF) SIR model for single-layered complex networks to the multiplex setting, where each layer has its own degree distribution and infection rate. Using the Next Generation Matrix method, we derive an analytical expression for the basic reproduction number $\mathcal R_0$. Numerical integration of the multiplex DBMF equations shows that $\mathcal R_0 = 1$ marks the epidemic threshold and governs the functional dependence of key outbreak indicators. In addition to the exact result for the $\mathcal R_0$, we provide an approximation denoted as $τ$, which is easier to compute and more straightforward to interpret in terms of the parameters of the system, and shares most of the expected properties of the basic reproduction number. Stochastic agent-based simulations confirm these results, demonstrating a direct correspondence between $τ$ and the average number of secondary infections in the early epidemic phase, in line with the interpretation of $\mathcal R_0$. This research provides a robust generalization of $\mathcal R_0$ for layered contact structures, offering a more realistic basis for epidemic forecasting and the design of intervention strategies. |
| title | Modeling Epidemics on Multiplex Networks: Epidemic Threshold and Basic Reproduction Number |
| topic | Physics and Society Statistical Mechanics Adaptation and Self-Organizing Systems Populations and Evolution |
| url | https://arxiv.org/abs/2510.12614 |