  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.
