Impact of temporary lockdown on disease extinction in assortative networks

TL;DR

This paper investigates how temporary lockdowns influence the likelihood of disease extinction in assortative networks using stochastic SIS models, semiclassical approximation, and simulations.

Contribution

It introduces a novel analysis of the impact of temporary lockdowns on disease extinction probabilities in correlated networks.

Findings

Lockdowns reduce disease extinction risk depending on their duration and strength.

Assortative network structure significantly influences the effectiveness of lockdown measures.

The study provides insights into optimal lockdown strategies for disease eradication.

Abstract

Changing environmental conditions can significantly affect the dynamics of disease spread. These changes may arise naturally or result from human interventions; in the latter case, lockdown measures that lead to abrupt but temporary reductions in transmission rates are used to combat disease spread. However, the impact of these measures on rare events in realistic populations has not been studied so far. Here, we analyze the susceptible-infected-susceptible (SIS) model in a stochastic setting where disease extinction -- a sudden clearance of the infection -- occurs via a rare, large fluctuation. We use a semiclassical approximation and extensive numerical simulations to show how the extinction risk of the disease depends on both the duration and magnitude of the lockdown, in heterogeneous assortative networks, with degree-degree correlations between neighboring nodes.