Viral Load-Driven Modeling of Epidemic Spread in Networks

Tingxuan Yang

Viral Load-Driven Modeling of Epidemic Spread in Networks

Tingxuan Yang

Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:453-460, 2025.

Abstract

This paper studies epidemic transmission in scale-free networks using an SIS model with viral load-dependent infectivity. A network disease model is developed and analyzed via HMF theory, deriving the basic reproduction number and its link to equilibrium stability. Simulations showing how viral load, network heterogeneity, and scale jointly affect transmission. Experiments indicate that: High-er initial viral load increases infection prevalence; larger degree exponent reduces infection due to low-degree node “transmission dead ends"; infection grows with network size in scale-free networks.

Cite this Paper

BibTeX

@InProceedings{pmlr-v278-yang25d,
  title = 	 {Viral Load-Driven Modeling of Epidemic Spread in Networks},
  author =       {Yang, Tingxuan},
  booktitle = 	 {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing},
  pages = 	 {453--460},
  year = 	 {2025},
  editor = 	 {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu},
  volume = 	 {278},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {25--27 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v278/main/assets/yang25d/yang25d.pdf},
  url = 	 {https://proceedings.mlr.press/v278/yang25d.html},
  abstract = 	 {This paper studies epidemic transmission in scale-free networks using an SIS model with viral load-dependent infectivity. A network disease model is developed and analyzed via HMF theory, deriving the basic reproduction number   and its link to equilibrium stability. Simulations showing how viral load, network heterogeneity, and scale jointly affect transmission. Experiments indicate that:  High-er initial viral load   increases infection prevalence; larger degree exponent reduces infection due to low-degree node “transmission dead ends"; infection grows with network size in scale-free networks.}
}

Endnote

%0 Conference Paper
%T Viral Load-Driven Modeling of Epidemic Spread in Networks
%A Tingxuan Yang
%B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing
%C Proceedings of Machine Learning Research
%D 2025
%E Nianyin Zeng
%E Ram Bilas Pachori
%E Dongshu Wang	
%F pmlr-v278-yang25d
%I PMLR
%P 453--460
%U https://proceedings.mlr.press/v278/yang25d.html
%V 278
%X This paper studies epidemic transmission in scale-free networks using an SIS model with viral load-dependent infectivity. A network disease model is developed and analyzed via HMF theory, deriving the basic reproduction number   and its link to equilibrium stability. Simulations showing how viral load, network heterogeneity, and scale jointly affect transmission. Experiments indicate that:  High-er initial viral load   increases infection prevalence; larger degree exponent reduces infection due to low-degree node “transmission dead ends"; infection grows with network size in scale-free networks.

APA

Yang, T.. (2025). Viral Load-Driven Modeling of Epidemic Spread in Networks. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:453-460 Available from https://proceedings.mlr.press/v278/yang25d.html.

Related Material

Download PDF