Intervention effect of targeted workplace closures may be approximated by single-layered networks in an individual-based model of COVID-19 control

Abstract

Abstract Individual-based models of infectious disease dynamics commonly use network structures to represent human interactions. Network structures can vary in complexity, from single-layered with homogeneous mixing to multi-layered with clustering and layer-specific contact weights. Here we assessed policy-relevant consequences of network choice by simulating different network structures within an established individual-based model of SARS-CoV-2 dynamics. We determined the clustering coefficient of each network structure and compared this to several epidemiological outcomes, such as cumulative and peak infections. High-clustered networks estimate fewer cumulative infections and peak infections than less-clustered networks when transmission probabilities are equal. However, by altering transmission probabilities, we find that high-clustered networks can essentially recover the dynamics of low-clustered networks. We further assessed the effect of workplace closures as a layer-targeted intervention on epidemiological outcomes and found in this scenario a single-layered network provides a sufficient approximation of intervention effect relative to a multi-layered network when layer-specific contact weightings are equal. Overall, network structure choice within models should consider the knowledge of contact weights in different environments and pathogen mode of transmission to avoid over- or under-estimating disease burden and impact of interventions.