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PreprintCC BY 4.0arXiv

Minimizing cumulative infections in SIS epidemic models over networks via an edge deletion algorithm

  • Hoang Phi Dung
  • Duong Khanh Ly
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Abstract

In this paper, we investigate the discrete SIS (Susceptible-Infected-Susceptible) models. We focus on minimizing epidemic spreading over networks by extending an existing edge deletion algorithm to the SIS model. To achieve this, we employ the mean-field approximation to linearize the network dynamics into a deterministic SIS model. We analytically demonstrate that the total number of infections is upper-bounded by a super-modular function, thereby ensuring the efficiency of the edge-deletion approach. To evaluate the proposed method, we conduct experiments on synthetic Erdos-Renyi networks and the real-world dataset collected from BBC Pandemic Haslemere app. Numerical simulations validate our theoretical results, confirming that both configurations converge to the stable, disease-free equilibrium.

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Cite

BibTeXRIS
BibTeX
@misc{dung2026minimizing,
  title = {{Minimizing cumulative infections in SIS epidemic models over networks via an edge deletion algorithm}},
  author = {Hoang Phi Dung and Duong Khanh Ly},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2606.30142},
  eprint = {2606.30142},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.30142},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Minimizing cumulative infections in SIS epidemic models over networks via an edge deletion algorithm
AU  - Hoang Phi Dung
AU  - Duong Khanh Ly
PY  - 2026
DA  - 2026/09/29
PB  - arXiv
DO  - 10.48550/arxiv.2606.30142
UR  - https://arxiv.org/abs/2606.30142
AB  - In this paper, we investigate the discrete SIS (Susceptible-Infected-Susceptible) models. We focus on minimizing epidemic spreading over networks by extending an existing edge deletion algorithm to the SIS model. To achieve this, we employ the mean-field approximation to linearize the network dynamics into a deterministic SIS model. We analytically demonstrate that the total number of infections is upper-bounded by a super-modular function, thereby ensuring the efficiency of the edge-deletion approach. To evaluate the proposed method, we conduct experiments on synthetic Erdos-Renyi networks and the real-world dataset collected from BBC Pandemic Haslemere app. Numerical simulations validate our theoretical results, confirming that both configurations converge to the stable, disease-free equilibrium.
ER  -