Social clustering in epidemic spread on coevolving networks.

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• Transitivity is a key feature of social networks, yet its role in epidemic spread on dynamic networks is understudied.
• Understanding network dynamics is crucial for effective disease control strategies.

Purpose of the Study:

• To investigate the influence of transitivity reinforcement on epidemic dynamics in coevolving networks.
• To model the interplay between infection spread and network structure changes.

Main Methods:

• Introduced an adaptive susceptible-infected-susceptible (SIS) epidemic model with coevolving infection and network structure.
• Incorporated a non-trivial probability of closing triangles during edge rewiring to enhance transitivity.
• Utilized numerical simulations and approximate master equations (AMEs) for analysis.

Main Results:

• AMEs accurately predict stationary-state disease prevalence and network degree distributions, especially when including transitivity reinforcement.
• AMEs can accurately track the temporal evolution of the system under certain parameters.
• Higher transitivity reinforcement correlates with lower prevalence of infective individuals when triangle closure dominates rewiring.

Conclusions:

• Transitivity plays a significant role in modulating SIS epidemic dynamics on coevolving networks.
• The developed model and AMEs offer accurate predictions and insights into epidemic behavior.
• Findings can inform strategies for controlling SIS-type epidemics by manipulating network transitivity.