Data-driven fair resource allocation for novel emerging epidemics: a COVID-19 Convalescent Plasma case study

AbstractEpidemics 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. 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 Randomized Controlled Trial (RCT) involving multiple hospital hubs across Canada (excluding Québec). 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 analyse the sensitivity of our model to different allocation settings and show that our model assigns equitable allocations across the entities.Keywords: Resource allocationepidemicsCOVID-19 Convalescent Plasmadata-driven optimizationdemand forecasting AcknowledgmentsThe authors would like to thank Julie Carruthers, Erin Jamula, and Melanie St John at the McMaster Centre for Transfusion Research for their administrative support.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementDue to the ethically, legally, and commercially sensitive nature of the research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.Additional informationFundingThis work was supported by Mitacs Research Training Award (Award IT22358), the McMaster Centre for Transfusion Research, and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant Program (RGPIN-2016-04518 and RGPIN-2022-02999).