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Logistics for diagnostic testing: An adaptive decision-support framework
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DOI：10.1016/j.ejor.2023.05.028.png)
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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 labora-tories. 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, lo-cal 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. Signif-icant improvements are achieved even when interventions in the decentralized assignment process only occur in response to increased pandemic activity.& COPY; 2023 Elsevier B.V. All rights reserved.
Keyword:
OR in health services
Decision support systems
Logistics
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
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