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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12231

Title: Comparison of Statistical Techniques for Forecasting Malaria Cases in Ghana
Authors: Twumasi-Ankrah, Sampson
Pels, WA
Nyantakyi, KA
Addo, DK
Keywords: Malaria Cases
Artificial Neural Network
Forecast Accuracy
Exponential Smoothing
Issue Date: Apr-2019
Publisher: Journal of Biostatistics and Biometric Applications
Citation: Journal of Biostatistics and Biometric Applications, Volume 4 | Issue 1
Abstract: Background and Aim: The purpose of this study was to determine an appropriate statistical technique for forecasting the monthly Malaria cases in Ghana. Methods and Materials: Monthly data spanning from January 2008 to December 2017 were obtained from the District Health Information Management System (DHIMS) 2, Ghana Health Service. The four competing forecasting techniques that were applied to the Malaria cases data were the Seasonal Autoregressive Integrated Moving Average (SARIMA), Artificial Neural Network (ANN), Exponential smoothing (ETS) and a Combination technique. The four competing forecasting techniques were compared using their respective forecast accuracy measures in order to choose the appropriate technique for forecasting Malaria cases in Ghana. Results: It was observed that the SARIMA technique was the appropriate statistical technique. The “best” model for forecasting the monthly malaria cases in Ghana was SARIMA (2, 1, 0) (2, 0, 0)12 which passed all the required diagnostic tests. Conclusion: A two-year monthly forecast from the “best” model revealed that, in 2018, we should expect a decrease in Malaria cases in the last quarter but should expect an increase in Malaria cases during the first half of 2019.
Description: This article is published in Journal of Biostatistics and Biometric Applications
URI: http://hdl.handle.net/123456789/12231
ISSN: Volume 4 | Issue 1
Appears in Collections:College of Science

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