Traffic-driven SIR epidemic spread dynamics on scale-free networks

Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible-Infected-Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks.

Automated summary

Traffic flow has a significant impact on the transmission and distribution of pathogens, especially in the context of global economic integration. This study adds new parameters to the traffic-driven Susceptible-Infected-Recovered (SIR) epidemic spread model to accurately represent the time characteristics of traffic-driven epidemic spread. By using a linear regression method on epidemic data in Hebei Province, the infection rate parameter is estimated, and an improved traffic-driven SIR epidemic spread dynamics model is established. The study investigates the effects of different link-closure rules, traffic flow, and network average degree on epidemic spread, finding that closing links between small-degree nodes is more effective in inhibiting the spread and reducing traffic flow and increasing network average degree can slow down the outbreak. The findings provide a practical scientific basis for traffic control measures during epidemic outbreaks.