Blood donation projections using hierarchical time series forecasting: the case of Zimbabwe's national blood bank.

BACKGROUND: The discrepancy between blood supply and demand requires accurate forecasts of the blood supply at any blood bank. Accurate blood donation forecasting gives blood managers empirical evidence in blood inventory management. The study aims to model and predict blood donations in Zimbabwe using hierarchical time series. The modelling technique allows one to identify, say, a declining donor category, and in that way, the method offers feasible and targeted solutions for blood managers to work on. METHODS: The monthly blood donation data covering the period 2007 to 2018, collected from the National Blood Service Zimbabwe (NBSZ) was used. The data was disaggregated by gender and blood groups types within each gender category. The model validation involved utilising actual blood donation data from 2019 and 2020. The model's performance was evaluated through the Mean Absolute Percentage Error (MAPE), uncovering expected and notable discrepancies during the Covid-19 pandemic period only. RESULTS: Blood group O had the highest monthly yield mean of 1507.85 and 1230.03 blood units for male and female donors, respectively. The top-down forecasting proportions (TDFP) under ARIMA, with a MAPE value of 11.30, was selected as the best approach and the model was then used to forecast future blood donations. The blood donation predictions for 2019 had a MAPE value of 14.80, suggesting alignment with previous years' donations. However, starting in April 2020, the Covid-19 pandemic disrupted blood collection, leading to a significant decrease in blood donation and hence a decrease in model accuracy. CONCLUSIONS: The gradual decrease in future blood donations exhibited by the predictions calls for blood authorities in Zimbabwe to develop interventions that encourage blood donor retention and regular donations. The impact of the Covid-19 pandemic distorted the blood donation patterns such that the developed model did not capture the significant drop in blood donations during the pandemic period. Other shocks such as, a surge in global pandemics and other disasters, will inevitably affect the blood donation system. Thus, forecasting future blood collections with a high degree of accuracy requires robust mathematical models which factor in, the impact of various shocks to the system, on short notice.

The Impact of Age Group in Hierarchical Forecasting of Monthly Blood Donations in Zimbabwe.

Background: To meet the blood requirements for transfusion therapy, blood banks need to ensure that blood inventories are maintained at desirable levels. There is a rising global need for optimal ways to manage blood supply and demand using statistical models in blood inventory planning and management. Thus, blood donation forecasting using donor-specific characteristics such as donor type and age is critical in managing the blood bank inventory. Methods: The monthly blood donation data covering the period 2007 to 2018, collected from the National Blood Service Zimbabwe (NBSZ) was used in this study. The data is first disaggregated based on donor age, and further disaggregation is performed for each age group based on donor type. The hierarchical forecasting approaches, namely the bottom-up, top-down and the optimal combination methods were used in the data analysis. The Error-Trend-Seasonality (ETS) and Autoregressive Integrated Moving Average (ARIMA) methods are used in the hierarchical forecasting approaches to generate the forecasts. Results: New blood donors account for more than 55% of blood donations in Zimbabwe. The younger donors (16-29 years) dominate the blood donations, accounting for 89.2% of the donations. Young and new donors account for nearly 50% of the donations. The middle-aged and older donors have lower blood donations. The bottom-up approach under the ARIMA model outperformed all the other approaches. The future projections show that new and young donors will increase in blood donations, regular donations will decline slightly while the occasional donations are projected to remain constant. Conclusion: Hierarchical forecasting is a unique approach in that the different aggregation levels reveal important features of the blood donation data. The lower percentage of regular donations is worrisome to blood authorities as it points to new blood donors not returning for further donations. Blood authorities need to develop policies that will encourage new and young donor categories to become regular donors.