Dynamics of infectious disease transmission in heterogeneous networks considering population interaction mechanism.

In public health emergencies, infectious disease transmission often varies significantly across populations. To investigate population heterogeneity and interaction mechanisms in cross-population transmission dynamics, we develop a community-based SIR (susceptible-infectious-removed) compartmental model applicable to multiple populations. This model is grounded in a heterogeneous network that incorporates both intra-population structural differences and inter-population coupling. Considering the structural characteristics and interactions between the service-providing and the served populations, we introduce multidimensional parameters, including contact preferences, different transmission rates, and recovery rates. The basic reproduction number is obtained analytically through the next-generation matrix method, with the stability of disease-free and endemic equilibria rigorously proved. Agent-based simulations and multi-scenario experiments are conducted to examine how network structure, coupling strength, and population migration mechanisms shape transmission dynamics. Finally, early-stage COVID-19 epidemic data from Hubei Province are used for parameter fitting, and the fitted parameters are employed to validate the Monte Carlo and Mean-field models, demonstrating the high accuracy of the model. This study shows that the coupling of the service population significantly accelerates epidemic transmission. Furthermore, population migration is identified as a transmission accelerator that exhibits a non-monotonic impact on the steady-state infection scale. However, under resource-constrained conditions, prioritizing the improvement of the recovery rate of small-scale service populations can indirectly reduce the final epidemic size in large-scale community populations. This study offers quantitative guidance for designing targeted interventions under multidimensional heterogeneity.