Susceptible-Infected-Susceptible Model with Mitigation on Scale-Free Networks
João Gabriel Simões Delboni, M. O. Hase
TLDR
This paper explores an SIS model on scale-free networks with a mitigation factor, revealing how it alters disease spread dynamics and prevalence.
Key contributions
- Integrates a Malthus-Verhulst-inspired mitigation factor into the SIS model on scale-free networks.
- Reveals mitigation induces a non-monotonic link infection probability, peaking at finite transmission rates.
- Demonstrates mitigation inverts the degree exponent's effect on prevalence, favoring larger exponents at high rates.
Why it matters
This research is crucial for understanding how behavioral responses or external interventions alter disease dynamics on complex networks. It provides new insights into epidemic control strategies, especially in environments with self-limiting spread.
Original Abstract
We investigate infectious disease spreading on scale-free networks using a heterogeneous mean-field approach applied to the susceptible-infected-susceptible model, incorporating a mitigation factor. Individual heterogeneity is incorporated through a power-law distribution, while a mitigation factor accounts for behavioral responses and external effects that effectively reduce transmission from infected individuals. This mechanism, inspired by Malthus-Verhulst-type constraints, introduces a nonlinear saturation effect that encodes self-limiting dynamics in a tractable way. Analytical results are supported by stochastic simulations. We find that the mitigation factor induces a nontrivial behavior in the probability that a link points to an infected node, which develops a maximum at finite infection rates. In contrast, the overall prevalence remains a monotonically increasing function of the transmission rate. Additionally, the mitigation mechanism leads to an inversion in the dependence of epidemic observables on the degree exponent at sufficiently high transmission rates. While in the standard model smaller exponents yield higher endemic prevalence, in the modified model this trend reverses, with larger exponents producing higher prevalence and increased infection probability along network links.
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