Analyses on ICU and non-ICU capacity of government hospitals during the COVID-19 outbreak via multi-objective linear programming: An evidence from Istanbul - Aydin, Nezir; Cetinkale, Zeynep (PERGAMON-ELSEVIER SCIENCE LTD, 2022)
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.
Keywords: Multi-objective; Resource optimization; Patient allocation; Uncertainty; COVID-19; Epidemic logistics; LOCATION OPTIMIZATION MODEL; VACCINE DISTRIBUTION; FACILITY LOCATION; SIMULATION; ALLOCATION; HEALTH; OPERATIONS; SERVICES; DISEASE; Life Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology