Epidemics are a serious public health threat, and the resources for mitigating their effects are typically limited. Decision-makers face challenges in forecasting the 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 without having to rely on demographic information. 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 a COVID-19 Convalescent Plasma (CCP) 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.
翻译:流行病是一种严重的公共卫生威胁,减轻其影响的资源通常有限,决策者在预测对这些资源的需求方面面临着挑战,因为以前关于该疾病的信息往往缺乏,疾病的行为可以(自然或由于公共卫生政策的结果)定期改变,并可能因地理区域而有所不同。在这项工作中,我们讨论了在不依赖人口信息的情况下,适合在突发爆发期间进行短期实时供应和需求预测的模式。我们提出了一个由数据驱动的混合整数方案规划(MIP)资源分配模式,该模式分配了现有资源,以最大限度地实现资源需求实体之间的公平概念。将我们的MIP模式应用于CVID-19相聚性白疹(CCP)案例研究的量化结果表明,我们的方法可以帮助平衡诸如CCP等有限产品的供需,并最大限度地减少需求实体未满足的需求比率。