
AbstractIn this article, we propose a stochastic network model for the spread of common sexually transmitted infections (STIs). The model expands the standard susceptible–infected–susceptible model by incorporating asymptomatic infected individuals—who are unaware that they are posing a health threat to themselves and the population—and individuals' behavioral preferences with regard to the use of protective measures during encounters. Behavioral preferences evolve according to a nontrivial mechanism accounting for the cost of using protection, the perceived risk, and persuasive effects due to sexual encounters with different‐minded individuals. The disease spreads on a time‐varying network of sexual contacts, generated using a mechanism inspired by continuous‐time activity‐driven networks. Such a network accounts for regular partners and casual encounters, which are regulated by a negotiation process that accounts for the individuals' behavioral preferences. Finally, three control measures are included in the model: (i) condom (social) marketing campaigns, (ii) routine screening at STI clinics, and (iii) partner notification. We use a mean‐field approach to analytically derive the epidemic threshold in the limit of large‐scale populations, in the absence of a partner network, for two distinct negotiation processes. Our results indicate that routine screening is key to the eradication of local outbreaks, while condom marketing campaigns become effective only when combined with screening. Monte Carlo simulations are then employed to extend our analytical findings, casting lights on the role of the partner network and on partner notification as a control measure in the spread of STIs.
Epidemiology, activity-driven network; epidemics models; nonlinear models and systems; sexually transmitted infections; stability, Systems biology, networks, epidemics models, nonlinear models and systems, activity-driven network, stability, sexually transmitted infections
Epidemiology, activity-driven network; epidemics models; nonlinear models and systems; sexually transmitted infections; stability, Systems biology, networks, epidemics models, nonlinear models and systems, activity-driven network, stability, sexually transmitted infections
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