The Network Limits of Infectious Disease Control via Occupation-Based Targeting

TL;DR

This paper develops methods to optimize pandemic control by leveraging occupational contact networks, aiming to balance disease containment with economic activity, and finds simple measures are nearly as effective as complex ones.

Contribution

It introduces a novel approach integrating occupational and network data into pandemic policy design, highlighting the potential for improved outcomes with minimal privacy concerns.

Findings

Significant gains in pandemic control and economic productivity are possible.

Simple occupational network measures perform nearly as well as complex, privacy-invasive measures.

Incorporating occupational contact data can enhance targeted intervention strategies.

Abstract

Policymakers commonly employ non-pharmaceutical interventions to manage the scale and severity of pandemics. Of non-pharmaceutical interventions, social distancing policies -- designed to reduce person-to-person pathogenic spread -- have risen to recent prominence. In particular, stay-at-home policies of the sort widely implemented around the globe in response to the COVID-19 pandemic have proven to be markedly effective at slowing pandemic growth. However, such blunt policy instruments, while effective, produce numerous unintended consequences, including potentially dramatic reductions in economic productivity. Here we develop methods to investigate the potential to simultaneously contain pandemic spread while also minimizing economic disruptions. We do so by incorporating both occupational and network information contained within an urban environment, information that is commonly excluded from typical pandemic control policy design. The results of our method suggest that large gains in both economic productivity and pandemic control might be had by the incorporation and consideration of simple-to-measure characteristics of the occupational contact network. However we find evidence that more sophisticated, and more privacy invasive, measures of this network do not drastically increase performance.