Infectious disease spreading modeling and containing strategy in heterogeneous population

Individual heterogeneity (i.e., age and underlying health condition) and contact pattern heterogeneity (i.e., circumstances and frequency of contact) are two crucial aspects of heterogeneous population, which have been demonstrated markedly affect the dynamical process and effectiveness of containing strategy of infectious disease. The former primarily influences susceptibility to the disease, while the latter predominantly controls the spreading process (e.g., "super-spreading" events often occur within households). In this study, we propose a contact data-driven infectious disease spreading model with age structure, underlying health conditions, and containing measures, as well as two types of heterogeneity-based containing strategies for heterogeneous populations. Based on this, a theoretical analysis framework is developed by extending mean-field theory. Using the next-generation matrix method, we estimate the basic reproduction number, finding it to be sensitive to individual heterogeneity, contact pattern heterogeneity, and containing measures. We perform extensive simulations for the spreading of Covid-19 and influenza in 31 cities and find that stronger household infection within contact pattern heterogeneity and the implementation of immunization both lead to a trade-off between the final cumulative infection density (daily infection density peak) and pandemic duration. Individual heterogeneity and contact pattern heterogeneity result in uneven distribution of infection density across different age groups and affect the immunization effects of different immunization strategies. When considering the impact of individual heterogeneity, immunizing only individuals without underlying diseases within each age group is insufficient to achieve optimal control; individuals with underlying diseases should be encouraged to get vaccinated as well. When considering the impact of contact pattern heterogeneity, the results from Anhui indicate that immunizing the 40–59 age group is most effective in the early stages of the epidemic, while overall, immunizing the 20–39 age group is most effective.