A Data-Driven Approach to Allocating Personal Protective Equipment During the COVID-19 Pandemic in King County, Washington

From the Field is a semiregular column that provides insight into the experiences of local, county, or state health professionals on the frontlines of health emergencies. National Association of County and City Health Officials members share the challenges faced and the solutions developed as they prepared for and responded to disasters, epidemics, and other major health issues. The aim of sharing these practical experiences is to provide other public health champions with the information and tools they need to help keep their communities safe even in extreme situations. The COVID-19 pandemic created an extraordinarily high demand for personal protective equipment (PPE). Acute need and supply chain disruptions made hospitals, emergency medical services, and other critical care agencies particularly vulnerable to PPE shortages. In March 2020, King County, Washington, developed computational tools, operating procedures, and data visualizations to fulfill its responsibilities to prioritize, allocate, and distribute scarce PPE equitably and efficiently during a public health emergency. King County distributed over 1.6 million gowns, 22 million gloves, 3.9 million surgical masks, and 1.5 million N95 respirators (among other items) during its PPE distribution mission. An algorithm processed resource requests from the community, with respect to available inventory, emergency allocation policies, prioritization constraints, estimated PPE use rates, agency-specific needs, and other parameters. With these inputs and constraints, the requests were translated into instructions for fulfillment and delivery and several tabular and graphical data visualizations were produced for quality assurance and transparency. Access to timely, relevant, and stable data was a constant challenge, and constraints invariably changed as the emergency response unfolded. King County's PPE distribution mission provides a useful case study in how to develop a scalable and data-driven approach to resource allocation and distribution under emergency response conditions.