Traffic-driven sequential spread of two interacting epidemics on complex networks.

Dynamics of pairwise-based SIS epidemic models on clustered networks with higher-order interactions

This work investigates the susceptible-infected-susceptible epidemic model on clustered regular networks using the pair mean-field approach, where higher-order effects are explicitly considered in dynamic analysis and proves the effectiveness of the pair mean-field method for analyzing higher-order network dynamics.

Applied Mathematical Modelling

Coevolution of epidemic dynamics and network topology driven by disease fatality and waning immunity

Epidemics on complex networks have been shown to exhibit dynamics that are strongly influenced by the topology of the network. However, it remains unclear how the topology of the network is influenced by epidemic properties, such as waning immunity and disease fatality, coupled with demographic changes. To explore the interplay between epidemic dynamics and network topology, we develop an agent-based model of a fatal infectious disease with an imperfect immunity on an initially scale-free network that evolves via demographic and epidemic-induced changes. We show that this model undergoes an epidemic transition between a phase in which the disease eventually dies out and a phase in which it becomes endemic. Moreover, the network may lose its scale-free property as a result of the epidemic spreading, giving rise to a topological transition from a power-law to a non-power-law degree distribution. We validate our findings using heterogeneous mean-field approximations of the agent-based model. These results highlight the importance of accounting for network evolution in epidemic models and advance our understanding of coevolving dynamical systems, in which disease dynamics and network topology continuously shape one another.

Turing instability and bistable epidemic spread on complex networks: Effects of topology and initial seeding.

A reaction-diffusion framework on complex networks is developed to investigate how local bistability, dispersal, and network topology jointly determine infection dynamics, revealing how local nonlinearities and network heterogeneity interact to shape epidemic transitions.

Dynamics of Information–Epidemic Coupled Spreading in Age-Heterogeneous Multilayer Networks

To investigate the interaction between protective information diffusion and disease transmission, this study proposes an information–epidemic coupled spreading model on an age-heterogeneous multilayer network. The population is divided into three age groups: youth, middle-aged individuals, and older adults. Disease transmission and protective information diffusion are represented in the physical disease layer and the virtual information layer, respectively. A UA-SIR framework is adopted to describe the coupled evolution of nodes’ information awareness states and epidemic states. The model further incorporates age-specific spreading parameters, the effects of awareness on infection risk and recovery processes, and the feedback effect of infection status on information diffusion. Node-level state transition equations are established, and the epidemic threshold is analyzed using an age-aggregated next-generation matrix. Simulation results show that age-dependent information diffusion, infection-induced feedback, the initial location of information seeds, and network topology all affect the epidemic spreading process. Empirical analyses based on four real infectious disease datasets further indicate that introducing information diffusion can reduce the infection peak, flatten the epidemic curve, and delay the peak time in some scenarios. These findings provide theoretical insights into age-stratified information intervention strategies in heterogeneous populations.

Attention to heterogeneous information sources affects epidemic outcomes.

During an epidemic, disease transmission and human behavior are closely related: High infection levels trigger risk awareness and preventive behaviors, which suppress the transmission of disease. While these coupled dynamics have been heavily investigated, both in well-mixed populations and networked populations, less has been done to explore how different ways to establish the awareness of disease affect infection levels. This study presents a mathematical model of two interacting groups (populations) to investigate how epidemic outcomes are affected by the choice of information source, i.e., one's own group and/or the other group. We demonstrate that for different physical contact patterns the system can undergo a bifurcation from a uniform endemic equilibrium to a nonuniform endemic equilibrium, resulting in a persistent infection level difference between groups that is driven by each group's choice of its information source. Our results reveal strategies to minimize infection: 1) a group with low infection level benefits by prioritizing information from a group with high infection level thereby overestimating risk and 2) a group with high infection level benefits by accessing information only about its own infection level thereby avoiding underestimation of risk. These results emphasize the need to promote awareness of disease levels in addition to observation of antiepidemic measures. We highlight conditions under which strategies that benefit one group may conflict with strategies that benefit the total infection level of both groups.