Effects of meteorological factors on blood donation

Shi Woei Lin, Kartika Nur Anisa, You Chen Chen

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Background and objectives: Blood donation can be affected by various factors including time, weather, and other events (such as disasters). To better understand how meteorological factors affect blood donation, this study builds a novel predictive model for blood supply in North Taiwan to facilitate better blood supply chain and inventory management. Materials and methods: This study uses a two-step prediction model that combines generalized additive models (GAM) and time series models to investigate the meteorological and the day-of-the-week effects on blood donation. For the period 2010–2014, blood donation data and weather information of Big Taipei area are described and analyzed. A bootstrap method was also implemented on the residuals produced by the two-step model to construct the prediction intervals. Results: Daily average temperature and daily cumulative precipitation have non-linear effects on blood donation. By including weather information into the model, the prediction model not only can account for the fluctuation affected by the day-of-the-week effects but also the weather (e.g., temperature and rainfall) effects. The prediction intervals can be used to detect the anomaly donation patterns or to signal possible shortages when there is a forecast of an extreme weather event. Conclusion: The findings underscore the need for quantitatively modeling meteorological effects in blood supply chain management, and provide important implications for the authority of a blood center to take necessary intervention and measures to reduce shortages and excessive expirations.

Original languageEnglish
Article number102901
JournalTransfusion and Apheresis Science
Volume59
Issue number6
DOIs
Publication statusPublished - Dec 2020
Externally publishedYes

Keywords

  • Blood donation prediction
  • Bootstrap
  • Generalized additive model
  • Time series

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