Modelling and Forecasting Malaria Mortality Rate using SARIMA Models (A Case Study of Aboh Mbaise General Hospital, Imo State Nigeria)
Science Journal of Applied Mathematics and Statistics
Volume 2, Issue 1, February 2014, Pages: 31-41
Received: Jan. 30, 2014; Published: Mar. 20, 2014
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Authors
Ekezie Dan Dan, Department of Statistics, Imo State University, Owerri, Nigeria
Opara Jude, Department of Statistics, Imo State University, Owerri, Nigeria
Okenwe Idochi, Department of Statistics, Rivers State Polytecnic, Bori, Rivers State, Nigeria
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Abstract
This paper examined the modeling and forecasting malaria mortality rate using SARIMA Models. Among the most effective approaches for analysing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). In this paper, we employed Box-Jenkins methodology to build ARIMA model for malaria mortality rate for the period January 1996 to December 2013 with a total of 216 data points. The model obtained in this paper was used to forecast monthly malaria mortality rate for the upcoming year 2014. The forecasted results will help Government and medical professionals to see how to maintain steady decrease of malaria mortality in other to combat the predicted rise in mortality rate envisaged in some months.
Keywords
ARIMA Model, SARIMA Model, Forecasting, ARMA Model, Box-Jenkins Methods, Malaria Mortality, Akaike Information Criteria, Bayesian Information Criterion
To cite this article
Ekezie Dan Dan, Opara Jude, Okenwe Idochi, Modelling and Forecasting Malaria Mortality Rate using SARIMA Models (A Case Study of Aboh Mbaise General Hospital, Imo State Nigeria), Science Journal of Applied Mathematics and Statistics. Vol. 2, No. 1, 2014, pp. 31-41. doi: 10.11648/j.sjams.20140201.15
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