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HomeArticlesVol 35,2025 No.11Detail

Prediction and analysis of red blood cells clinical demand based on the ARIMA model

Published on Nov. 28, 2025Total Views: 85 timesTotal Downloads: 37 timesDownloadMobile

Author: YANG Xu 1 LIU Shuqi 2 HUANG Hui 3 CHEN Xiulan 4 LOU Yiling 5 CAO Shiyi 5 JIANG Qingqing 5, 6

Affiliation: 1. Office of Wuhu City Central Blood Station, Wuhu 241000, Anhui province, China 2. School of Laboratory Medicine, Wannan Medical College, Wuhu 241002, Anhui province, China 3. Departments of Laboratory, Wuhu City Central Blood Station, Wuhu 241000, Anhui province, China 4. Departments of Component, Wuhu City Central Blood Station, Wuhu 241000, Anhui province, China 5. School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China 6. School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China

Keywords: Red blood cell ARIMA multiplicative seasonal model Demand forecasting Health management

DOI: 10.12173/j.issn.1004-5511.202412034

Reference: Yang X, Liu SQ, Huang H, Chen XL, Lou YL, Cao SY, Jiang QQ. Prediction and analysis of red blood cells clinical demand based on the ARIMA model[J]. Yixue Xinzhi Zazhi, 2025, 35(11): 1272-1278. DOI: 10.12173/j.issn.1004-5511.202412034. [Article in Chinese]

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Abstract

Objective  To construct an ARIMA multiplicative seasonal model suitable for predicting the clinical demand of red blood cells in Wuhu City, and provide a scientific basis for blood collection organizations to formulate red blood cell collection and supply balance programs and recruitment plans.

Methods  The ARIMA model was constructed based on the clinical use of red blood cells in Wuhu City Central Blood Station from January 2012 to December 2021. The data were processed through time series stabilization, model identification, and parameter verification to determine the optimal model. The red blood cell clinical use demand from January 2022 to August 2022 was predicted using the optimal model, and the prediction effect was verified using actual values.

Results  The optimal model was ARIMA (0,1,0) (1,1,0)12, with BIC=12.162. The ACF and PACF of the residual sequence were basically within the 95% confidence interval, and the Ljung-Box Q value was 15.265, with P=0.576>0.05, which met the requirements of white noise sequence, and the model fitting was validated. Except for April and May, the actual values of each month were within the 95% confidence interval of the predicted value, and the average relative error between the ARIMA model prediction value and the actual value was -0.00375, and the mean absolute percentage error (MAPE) was 7.087%, with good prediction effect.

Conclusion  The ARIMA (0,1,0) (1,1,0)12 model can be used to predict the clinical demand of red blood cells, and it can provide a reference for blood collection, non-remunerated blood donation recruitment and inventory management.

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