Diagnostic testing is a fundamental component in effective outbreak containment during every phase of a pandemic. Test samples are collected at testing facilities and subsequently analyzed at specialized laboratories. In high-income countries where health care providers are often privately owned, the assignments of samples from testing facilities to laboratories are determined by individual stakeholders. While this decentralized system effectively matches supply and demand during normal times, dispersed outbreaks, e.g., as encountered during the COVID-19 pandemic, lead to imbalanced requests for diagnostic capacity. With no coordinating entity in place to match demands at testing facilities to laboratory capacities, local backlogs build up rapidly thus increasing waiting times for test results and thus impeding subsequent containment efforts. To ease the impact of erratic regional outbreaks through improved logistics activities, we develop a rolling horizon framework which repeatedly solves a mathematical programming snapshot problem based on the current number of test samples. The procedure dynamically adapts to requirements resulting from the pandemic activity and supports rather than replaces decentralized operations in order to match testing requests with available laboratory capacities. We present problem-specific performance indicators and assess the quality of our procedure in a case study based on the COVID-19 outbreak in 2020 in Germany. Experimental results demonstrate the potential of coordinating mechanisms to support the logistics related to diagnostic testing and hence to reduce waiting times for PCR test results. Significant improvements are achieved even when interventions in the decentralized assignment process only occur in response to increased pandemic activity.

诊断测试是大流行各个阶段有效遏制疫情的基本组成部分。测试样品在测试设施中收集，随后在专业实验室进行分析。在医疗保健提供者通常为私营的高收入国家，样本从检测设施到实验室的分配由各个利益相关者决定。虽然这种去中心化系统在正常情况下可以有效匹配供需，但分散的疫情爆发（例如在 COVID-19 大流行期间遇到的情况）会导致对诊断能力的需求不平衡。由于没有协调实体来匹配检测设施的需求和实验室能力，当地积压的案件迅速增加，从而增加了检测结果的等待时间，从而阻碍了后续的遏制工作。为了通过改善物流活动来缓解不稳定的区域性疫情的影响，我们开发了一个滚动范围框架，该框架根据当前的测试样本数量反复解决数学规划快照问题。该程序动态地适应大流行活动产生的要求，并支持而不是取代分散的操作，以便将测试请求与现有的实验室能力相匹配。我们在基于 2020 年德国 COVID-19 爆发的案例研究中提出了特定问题的绩效指标并评估了我们的程序质量。实验结果证明了协调机制支持诊断检测相关物流的潜力，从而减少 PCR 检测结果的等待时间。