raymond essuman

Studies have shown periodic variations in the number of births using different mathematical models. A study conducted at the Korle-Bu teaching hospital obtained Seasonal Autoregressive Integrated Moving Average (SARIMA) model on a monthly number of birth for an 11-year data. However, this study did not compare the obtained model with other forecasting methods to determine the method that will best explain the data. This study sought to compare seasonal SARIMA model with Holt-Winters seasonal forecasting methods for an 11-year time series data on the number of births..

Data were analysed in R software (version 3.3.3). Holt-Winters and seasonal ARIMA forecasting methods were applied to the birth data. The errors of the out – of-sample forecast of these methods were compared and the one with the least error was considered the best forecasting method.

The in-sample forecasting errors showed that SARIMA (2,1,1) x (1,01,) was the best among the other models. The out-of-sample errors also showed that all the SARIMA models had lower errors compared to the Holt-Winters form of additive and multiplicative methods based on the forecasting accuracy indices of the monthly number of births for an 11-year period. It was also found that the months with very high statistically significant number of births over the period was from March to August.

The SARIMA models were superior to the Holt-Winters models. This is essential for optimal forecasting of the number of births for planning and effective delivery of Obstetrics services..

Changes in the trend of births among women have been studied worldwide with indications of peaks and troughs over a specified period. Periodic variations in the number of births among women are unknown at the Korle-Bu Teaching Hospital (KBTH). This study sought to model and predicts monthly number of births at the Department of Obstetrics and Gynaecology (O&G), KBTH.
Methods & Materials: Box-Jenkins time series model approach was applied to an 11-year data from the Department of (O&G), KBTH on the number of births from January, 2004 to December, 2014. Box-Jenkins approach was put forward as autoregressive integrated moving average (ARIMA) model. Several possible models were formulated, and the best model, which has the smallest Akaike information criterion corrected (AICc) was selected. The best model was then used for future predictions on the expected monthly number of births for the year 2015. Analysis was performed in R statistical software (version 3.0.3).
Seasonal ARIMA (2,1,1) × (1,0,1)12 was selected as the best model because it had the smallest AICc. Furthermore, the forecasted values showed that the expected number of births were lowest in January (750 births) and highest in May (970 births) for the year 2015.
Seasonal ARIMA (2,1,1) × (1,0,1)12 was identified as the model that best describes monthly expected births and its use to forecast the expected number of births at the KBTH in Ghana will facilitate formulation of health policies and planning for safe maternal delivery and prudent use of hospital obstetric services and facilities.