Enhancing ‘disease x’ responsiveness: a risk-averse distributionally robust optimization approach for resource allocation under infectious disease transmission variability
针对传染病大流行中检测试剂盒的分配问题，考虑需求时空不确定性和概率分布模糊性，提出两阶段分布鲁棒优化模型，并加入风险规避准则，以美国新冠检测试剂盒分配为例验证模型实用性。
Testing stands out as a crucial element in the public health response during a pandemic, serving diverse purposes pivotal for controlling the spread of the infectious agent. It enables early case detection, facilitating prompt isolation and treatment, thereby reducing the severity of individual cases and interrupting the transmission chain within communities. Recognizing the pivotal role of testing in managing and mitigating the impact of a pandemic, this paper addresses the distribution of test kits, taking into account the spatiotemporal uncertainty in demand and the ambiguity of the demand's probability distribution over a multi-period horizon. Determining decisions about location, allocation, operational distribution, and shipment is the main goal in order to guarantee equity and fairness in the distribution of resources among different populations. A two-stage distributionally robust optimisation (DRO) model is proposed to address the ambiguity in the probability distribution of demand. An equivalent reformulation is derived for this model over L1-norm and joint L1- and L∞-norm ambiguity sets. Additionally, a risk-averse criterion is employed to more accurately account for some of the worst realizations of random future demand scenarios. To demonstrate the proposed DRO model's practicality, a numerical analysis of COVID-19 test kit distribution in the US is carried out.