Enhancement of large fluctuations to extinction in adaptive networks

TL;DR

This paper investigates how adaptive rewiring in networks affects the likelihood and size of large fluctuations leading to infection extinction, revealing that increased rewiring significantly enhances extinction probability.

Contribution

It introduces a large-deviation theory approach to quantify the impact of adaptive rewiring on epidemic fluctuations and extinction times in network models.

Findings

Small rewiring sharply reduces mean extinction times.

Increased rewiring exponentially boosts large fluctuation probabilities.

Adaptive networks exhibit higher chances of epidemic extinction with more rewiring.

Abstract

During an epidemic, individual nodes in a network may adapt their connections to reduce the chance of infection. A common form of adaption is avoidance rewiring, where a noninfected node breaks a connection to an infected neighbor and forms a new connection to another noninfected node. Here we explore the effects of such adaptivity on stochastic fluctuations in the susceptible-infected-susceptible model, focusing on the largest fluctuations that result in extinction of infection. Using techniques from large-deviation theory, combined with a measurement of heterogeneity in the susceptible degree distribution at the endemic state, we are able to predict and analyze large fluctuations and extinction in adaptive networks. We find that in the limit of small rewiring there is a sharp exponential reduction in mean extinction times compared to the case of zero adaption. Furthermore, we find an exponential enhancement in the probability of large fluctuations with increased rewiring rate, even when holding the average number of infected nodes constant.