Maintaining Healthcare Capacity in Rural America by Replenishing Personal Protective Equipment: The Case from West Virginia.

This article focuses on the development and implementation of novel forecasting and inventory management approaches to address the challenge of supplying personal protective equipment (PPE) in West Virginia during the COVID-19 pandemic. It describes a joint interagency task force (JIATF) that created an agent-based epidemiological model called S(HS)IR (susceptible-(hospitalized suspected-susceptible)-infectious-recovered) to improve PPE demand forecasts by including both confirmed and suspected COVID-19 patients, addressing limitations of existing models that exclude suspected cases. To manage PPE inventory under nonstandard lead times and autocorrelated demand forecasts, the authors developed a nonparametric random-length moving-block bootstrap (RLMBB) method for setting safety stock and reorder points, which outperformed traditional approaches in simulation studies. The article details the practical implementation of these methods in West Virginia’s rural healthcare context, highlighting challenges such as data integration barriers and the importance of localized data, and discusses the broader applicability of these approaches to future public health emergencies and supply chain settings.