TY - JOUR
TI - Dynamic simulation and policy optimization of epidemic spread in heterogeneous networks: A study based on the susceptible-infectious-recovered-susceptible model
AU - Luo Zhibin
AU - Li Haiyan
AU - Li Haibin
JN - Thermal Science
PY - 2026
VL - 30
IS - 2
SP - 1039
EP - 1045
PT - Article
AB - This study investigates the dynamic processes of epidemic spread and policy  optimization in heterogeneous Barabasi-Albert networks using the  susceptible-infectious-recovered-susceptible model. By systematically  adjusting key parameters, including the number of inter-subnetwork  connections, the number of sub-networks, and node connection strategies, we  conducted a comprehensive analysis of the impact of these factors on  epidemic transmission. This analysis revealed the critical role of network  structure in disease spread. The findings indicate that augmenting  inter-subnetwork connections and node connections expedites the propagation  of the epidemic, culminating in elevated infection peaks but diminished  overall epidemic durations. The model validation is further substantiated by  the use of real-world data from the outbreak of the novel coronavirus  disease in Wuhan, China, with the simulation results demonstrating a close  alignment with the observed trends. This finding serves to substantiate the  model's efficacy. Studies on policy optimization have indicated that the  premature relaxation of control measures can result in elevated infection  peaks. Conversely, the easing of measures at opportune times can facilitate  more effective epidemic control. This research establishes a theoretical  framework for public health decision-making, particularly in terms of  balancing epidemic control with socio-economic recovery.
DO - 10.2298/TSCI2602039L
ER -