Data-driven Fair Resource Allocation For Novel Emerging Epidemics: A COVID-19 Convalescent Plasma Case Study

Abstract

Epidemics are a serious public health threat, and the resources for mitigating their effects are typically limited. Decision-makers face challenges in forecasting the supply and demand for these resources as prior information about the disease is often not available, the behaviour of the disease can periodically change (either naturally or as a result of public health policies) and can differ by geographical region. Randomized controlled trials (RCTs) using scarce resources such as blood products as a randomized intervention are affected by epidemics. In this work, we discuss a model that is suitable for short-term real-time supply and demand forecasting during emerging outbreaks. We consider a case study of demand forecasting and allocating scarce quantities of COVID-19 Convalescent Plasma (CCP) in an international multi-site RCT involving multiple hospital hubs across Canada (excluding Qu\'ebec). We propose a data-driven mixed-integer programming (MIP) resource allocation model that assigns available resources to maximize a notion of fairness among the resource-demanding entities. Numerical results from applying our MIP model to the case study suggest that our approach can help balance the supply and demand of limited products such as CCP and minimize the unmet demand ratios of the demand entities. We analyze the sensitivity of our model to different allocation settings and show that our model assigns equitable allocations across the entities.