Mitigating the COVID‐19 pandemic through data‐driven resource sharing

Abstract

COVID‐19 outbreaks in local communities can result in a drastic surge in demand for scarce resources such as mechanical ventilators. To deal with such demand surges, many hospitals (1) purchased large quantities of mechanical ventilators, and (2) canceled/postponed elective procedures to preserve care capacity for COVID‐19 patients. These measures resulted in a substantial financial burden to the hospitals and poor outcomes for non‐COVID‐19 patients. Given that COVID‐19 transmits at different rates across various regions, there is an opportunity to share portable healthcare resources to mitigate capacity shortages triggered by local outbreaks with fewer total resources. This paper develops a novel data‐driven adaptive robust simulation‐based optimization (DARSO) methodology for optimal allocation and relocation of mechanical ventilators over different states and regions. Our main methodological contributions lie in a new policy‐guided approach and an efficient algorithmic framework that mitigates critical limitations of current robust and stochastic models and make resource‐sharing decisions implementable in real‐time. In collaboration with epidemiologists and infectious disease doctors, we give proof of concept for the DARSO methodology through a case study of sharing ventilators among regions in Ohio and Michigan. The results suggest that our optimal policy could satisfy ventilator demand during the first pandemic's peak in Ohio and Michigan with 14% (limited sharing) to 63% (full sharing) fewer ventilators compared to a no sharing strategy (status quo), thereby allowing hospitals to preserve more elective procedures. Furthermore, we demonstrate that sharing unused ventilators (rather than purchasing new machines) can result in 5% (limited sharing) to 44% (full sharing) lower expenditure, compared to no sharing, considering the transshipment and new ventilator costs.