Disease Transmission on Random Graphs Using Edge-Based Percolation
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909580271812608 |
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| author | Zhao, S. Magpantay, F. M. G. |
| author_facet | Zhao, S. Magpantay, F. M. G. |
| contents | Edge-based percolation methods can be used to analyze disease transmission on complex social networks. This allows us to include complex social heterogeneity in our models while maintaining tractability. Here we review the seminal works on this field by Newman et al (2001); Newman (2002, 2003), and Miller et al (2012). We present a systematic discussion of the theoretical background behind these models, including an extensive derivation of the major results. We also connect these results relate back to the classical literature in random graph theory Molloy and Reed (1995, 1998). Finally, we also present an accompanying R package that takes epidemic and network parameters as input and generates estimates of the epidemic trajectory and final size. This manuscript and the R package was developed to help researchers easily understand and use network models to investigate the interaction between different community structures and disease transmission. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Disease Transmission on Random Graphs Using Edge-Based Percolation Zhao, S. Magpantay, F. M. G. Social and Information Networks Dynamical Systems Populations and Evolution 00A71, 37N25, 92D25, 92D30 Edge-based percolation methods can be used to analyze disease transmission on complex social networks. This allows us to include complex social heterogeneity in our models while maintaining tractability. Here we review the seminal works on this field by Newman et al (2001); Newman (2002, 2003), and Miller et al (2012). We present a systematic discussion of the theoretical background behind these models, including an extensive derivation of the major results. We also connect these results relate back to the classical literature in random graph theory Molloy and Reed (1995, 1998). Finally, we also present an accompanying R package that takes epidemic and network parameters as input and generates estimates of the epidemic trajectory and final size. This manuscript and the R package was developed to help researchers easily understand and use network models to investigate the interaction between different community structures and disease transmission. |
| title | Disease Transmission on Random Graphs Using Edge-Based Percolation |
| topic | Social and Information Networks Dynamical Systems Populations and Evolution 00A71, 37N25, 92D25, 92D30 |
| url | https://arxiv.org/abs/2401.06872 |