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.