Intermittent social distancing strategy for epidemic control

TL;DR

This paper investigates an intermittent social distancing strategy in epidemic models on complex networks, demonstrating its effectiveness in preventing outbreaks through theoretical analysis and simulations.

Contribution

It introduces a local-information-based intermittent social distancing method and identifies a threshold parameter for epidemic suppression.

Findings

Existence of a cutoff threshold for social distancing effectiveness

Strategy induces a susceptible herd behavior

Percolation theory explains the results

Abstract

We study the critical effect of an intermittent social distancing strategy on the propagation of epidemics in adaptive complex networks. We characterize the effect of our strategy in the framework of the susceptible-infected-recovered model. In our model, based on local information, a susceptible individual interrupts the contact with an infected individual with a probability $\sigma$ and restores it after a fixed time $t_{b}$. We find that, depending on the network topology, in our social distancing strategy there exists a cutoff threshold $\sigma_{c}$ beyond which the epidemic phase disappears. Our results are supported by a theoretical framework and extensive simulations of the model. Furthermore we show that this strategy is very efficient because it leads to a "susceptible herd behavior" that protects a large fraction of susceptibles individuals. We explain our results using percolation arguments.