Minimizing cumulative infections in SIS epidemic models over networks via an edge deletion algorithm
2026-06-29 • Social and Information Networks
Social and Information Networks
AI summaryⓘ
The authors study how diseases spread in networks using a simplified SIS (Susceptible-Infected-Susceptible) model. They improve a method that removes certain connections (edges) in the network to reduce how much the disease spreads. By using math to approximate the situation, they prove that their method works efficiently in limiting infections. Tests on both made-up and real-world network data show their approach helps the system settle into a state without infections.
SIS modelepidemic spreadingnetwork dynamicsmean-field approximationedge deletionsuper-modular functionErdos-Renyi networksdisease-free equilibrium
Authors
Phi Dung Hoang, Khanh Ly Duong
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.