Department of Industrial Engineering, Yildiz Technical University, Besiktas, 34349, Istanbul, Turkey.
Department of Industrial Engineering, Yildiz Technical University, Besiktas, 34349, Istanbul, Turkey; Turkish Airlines, 34149, Yesilkoy, İstanbul, Turkey.
Comput Biol Med. 2022 Jul;146:105562. doi: 10.1016/j.compbiomed.2022.105562. Epub 2022 May 6.
The current infectious disease outbreak, a novel acute respiratory syndrome [SARS]-CoV-2, is one of the greatest public health concerns that the humanity has been struggling since the end of 2019. Although, dedicating the majority of hospital-based resources is an effective method to deal with the upsurge in the number of infected individuals, its drastic impact on routine healthcare services cannot be underestimated. In this study, the proposed multi-objective, multi-period linear programming model optimizes the distribution decision of infected patients and the evacuation rate of non-infected patients simultaneously. Moreover, the presented model determines the number of new COVID-19 intensive care units, which are established by using existing hospital-based resources. Three objectives are considered: (1) minimization of total distance travelled by infected patients, (2) minimization of the maximum evacuation rate of non-infected patients and (3) minimization of the infectious risk of healthcare professionals. A case study is performed for the European side of Istanbul, Turkey. The effect of the uncertain length of the stay of infected patients is demonstrated via sensitivity analyses.
当前的传染病疫情是自 2019 年底以来人类面临的最大公共卫生问题之一，一种新型急性呼吸道综合征 [SARS]-CoV-2。尽管将大多数基于医院的资源用于应对感染人数的激增是一种有效的方法，但它对常规医疗保健服务的巨大影响不容忽视。在这项研究中，所提出的多目标、多周期线性规划模型同时优化了感染患者的分配决策和非感染患者的疏散率。此外，所提出的模型确定了新的 COVID-19 重症监护病房的数量，这些病房是利用现有的医院资源建立的。考虑了三个目标：（1）感染患者总旅行距离的最小化，（2）非感染患者最大疏散率的最小化，以及（3）医疗保健专业人员感染风险的最小化。对土耳其伊斯坦布尔欧洲部分进行了案例研究。通过敏感性分析演示了感染患者逗留时间不确定的影响。
