Time Series Modeling of Blood in Demand for Kurunegala District, Sri Lanka
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Date
2020
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Uva Wellassa University of Sri Lanka
Abstract
In general, blood comes into four main Groups; O, A, B, and AB. The most common
and highly demanded blood is Group O. Blood can also be subdivided into its main
components; red cells, white cells, platelets, and plasma. Unfortunately, red cells only
have a shelf-life of 35 or 42 days, while platelet shelf life is even less, only five
days. Blood cells are essential components of the human body. Blood cannot be
manufactured mechanically and can only be obtained by donation. Human blood
pressure and heart rate will stay close to normal as one human loss up to 30% of blood.
If they lose more than 40% of blood they will die. It’s important to get to a hospital to
start receiving blood transfusion to prevent this. Blood transfusion is generally required
in surgeries, childbirths, organ transplants, and for patients who are receiving treatments
for diseases such as cancers and anaemia. Therefore, it is essential to study the blood in
demand for the near future. According to Sri Lanka, the National Blood Transfusion
Service (NBTS) is the sole supplier of blood and blood products to all state hospitals and
it has ninety-six blood Banks Island-wide. This study investigates to develop a suitable
time series model for the monthly blood demand for Kurunegala district. The data was
obtained from the NBTS Sri Lanka, which consists of the monthly demand for red blood
cells from January 2011 to November 2017. The modeling has been done using the BoxJenkin’s Auto-Regressive Integrated Moving Average (ARIMA) procedure. Moreover,
to identify the best fitting model, Akaike Information Criterion (AIC), Bayesian
Information Criterion (BIC) and Mean Square Error (MSE) were used. Through the
analysis, it was identified that ARIMA (0, 1, 1) is the most appropriate model for the
monthly blood in demand for the Kurunegala district.
Key words: Blood in demand, Blood groups, Red cells, ARIMA
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Health Science, medical science