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• W4292158059 endingPage "100078" @default.
• W4292158059 startingPage "100078" @default.
• W4292158059 abstract "COVID-19 has caused negative impacts on blood supply chain management, due to uncertain supply/demand and logistical disruptions. In the early weeks following the COVID-19 pandemic, 20–30% reduction in blood donation had observed, which adversely affected the whole blood supply chain. Although this shortage was partially compensated through rescheduling of elective surgeries and shifting some inpatient surgeries to outpatient surgeries, resumption of the normal surgeries by hospitals had increased the demands for the blood products. At the same time, the total blood supply was increased by some measures taken to overcome the blood shortage. In this paper, a multivariate time-series deep learning model based on long short-term memory is proposed to forecast the blood donation/demand. It takes daily time-series of blood donation/demand (internal features) as well as daily time-series of new confirmed COVID-19 cases/deaths (external features) as its inputs, and predicts blood donation/demand for the next week. The proposed model is used to achieve a resilient blood inventory management, capable of handling the uncertainties occurring during the COVID-19 pandemic. The proposed blood donation/demand forecasting model has been successfully simulated on the collected data of Tehran Blood Center in Tehran, Iran, for a time period from February 24, 2020, to October 14, 2021. Obtained results show the efficiency of the proposed model by obtaining 6.1% and 6.5% error between the actual and forecasted values of the number of donations and demands, respectively. The results of applying the proposed model for inventory management of blood platelets demonstrate the resiliency of our model to reduce shortage and wastage rates against the existing uncertainty handling models by 32.1% and 26.6%, respectively. The proposed method can be used to assist the decision makers in managing the blood supply chains through prioritizing the blood transfusions during COVID-19 and similar pandemics in the future." @default.
• W4292158059 created "2022-08-18" @default.
• W4292158059 creator A5002859484 @default.
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• W4292158059 date "2022-12-01" @default.
• W4292158059 modified "2023-09-30" @default.
• W4292158059 title "Multivariate time-series blood donation/demand forecasting for resilient supply chain management during COVID-19 pandemic" @default.
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• W4292158059 doi "https://doi.org/10.1016/j.clscn.2022.100078" @default.
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