  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.
