Abstract

The global scientific community has intensified efforts to develop, test, and commercialize pharmaceutical products to deal with the COVID-19 pandemic. Trials for both antivirals and vaccines are in progress; candidates include existing repurposed drugs that were originally developed for other ailments. Once these are shown to be effective, their production will need to be ramped up rapidly to keep pace with the growing demand as the pandemic progresses. It is highly likely that the drugs will be in short supply in the interim, which leaves policymakers and medical personnel with the difficult task of determining how to allocate them. Under such conditions, mathematical models can provide valuable decision support. In particular, useful models can be derived from process integration techniques that deal with tight resource constraints. In this paper, a linear programming model is developed to determine the optimal allocation of COVID-19 drugs that minimizes patient fatalities, taking into account additional hospital capacity constraints. Two hypothetical case studies are solved to illustrate the computational capability of the model, which can generate an allocation plan with outcomes that are superior to simple ad hoc allocation.

Graphic abstract

中文翻译：

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新挑战的过程集成：资源限制下抗病毒药物的最佳分配。

摘要

全球科学界已加紧努力开发、测试和商业化药品以应对 COVID-19 大流行。抗病毒药物和疫苗的试验正在进行中；候选药物包括最初为其他疾病开发的现有再利用药物。一旦这些被证明是有效的，它们的产量将需要迅速增加，以跟上随着大流行的发展而不断增长的需求。这些药物极有可能在此期间供不应求，这使决策者和医务人员难以确定如何分配它们。在这种情况下，数学模型可以提供有价值的决策支持。特别是，有用的模型可以从处理严格资源约束的过程集成技术中获得。在本文中，开发了一个线性规划模型来确定 COVID-19 药物的最佳分配，以最大限度地减少患者死亡，同时考虑到额外的医院容量限制。解决了两个假设的案例研究来说明模型的计算能力，它可以生成一个分配计划，其结果优于简单的临时分配。

图形摘要