Modeling partial lockdowns in multiplex networks using partition strategies

TL;DR

This paper models partial lockdowns in multiplex social networks using partition strategies to balance epidemic control and economic impact, highlighting the importance of social interaction restrictions.

Contribution

It introduces a multiplex network model with partition strategies for partial lockdowns, analyzing their effects on epidemic spread and economic costs.

Findings

Partition strategies can effectively reduce epidemic spread.

Unconstrained social interactions significantly increase transmission.

Restrictions on social interactions improve lockdown effectiveness.

Abstract

National stay-at-home orders, or lockdowns, were imposed in several countries to drastically reduce the social interactions mainly responsible for the transmission of the SARS-CoV-2 virus. Despite being essential to slow down the COVID-19 pandemic, these containment measures are associated with an economic burden. In this work, we propose a network approach to model the implementation of a partial lockdown, breaking the society into disconnected components, or partitions. Our model is composed by two main ingredients: a multiplex network representing human contacts within different contexts, formed by a Household layer, a Work layer, and a third Social layer including generic social interactions, and a Susceptible-Infected-Recovered process that mimics the epidemic spreading. We compare different partition strategies, with a twofold aim: reducing the epidemic outbreak and minimizing the economic cost associated to the partial lockdown. We also show that the inclusion of unconstrained social interactions dramatically increases the epidemic spreading, while different kinds of restrictions on social interactions help in keeping the benefices of the network partition.