Abstract
Background and Objectives: Blood transfusion is a widely accepted treatment modality in modern medical practice and it has no substitute. Therefore, blood is a scarce resource, and proper management of bloodstock is essential. Transfusion service is responsible to maintain an adequate blood stock to ensure the supply of blood for hospitals while minimizing blood wastage due to postexpiry. To achieve efficient bloodstock management, the pattern of blood collection should be identified. This study was designed to establish a time series model for monthly blood collection of Sri Lanka. Methods: Data on monthly blood collection of Sri Lanka were collected from the year 2010 to 2020 and time series models were developed using “R” statistical software. Results: Time series data clearly exhibited an increasing trend with seasonality in blood collection. Therefore, seasonal time series models were fitted and the best seasonal autoregressive integrated moving average (ARIMA) model was selected as ARIMA (0, 1, 1) (0, 1, 2) (12) which showed the lowest Akaike information criteria value. Conclusion: It is suitable for forecasting the monthly blood collection.
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