Complex dynamics of synergistic coinfections on realistically clustered networks

Area of Science:

• Epidemiology
• Mathematical Biology
• Network Science

Background:

• Coinfection dynamics and contact network structures are typically studied separately.
• Disease propagation is generally hindered by contact network clustering.

Purpose of the Study:

• To investigate how contact structure clustering affects the dynamics of multiple interacting diseases.
• To explore the conditions under which clustering can accelerate epidemic spread in coinfection scenarios.

Main Methods:

• Developed a mean-field model for coinfection of two diseases with susceptible-infectious-susceptible dynamics.
• Analyzed interactions on modular networks and introduced a tertiary infection criterion.

Main Results:

• Clustering can unexpectedly speed up epidemic propagation in synergistic coinfections when coupling strength matches clustering.
• Observed first-order transitions in endemic states, leading to explosive outbreaks.
• Identified clustered networks as unique locations for explosive outbreaks under specific conditions.

Conclusions:

• Clustering's impact on disease dynamics is complex and context-dependent, especially in coinfection scenarios.
• Results highlight the importance of considering network structure in epidemiological models.
• Called for more detailed epidemiological data on interacting coinfections.