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Research Article Open access CC BY 3.0

Analysis and Modeling of Prevalence of Measles in the Ashanti Region of Ghana

Kwame Asare Gyasi-Agyei, William Obeng Denteh, Amoakoh Gyasi-Agyei

Journal of Advances in Mathematics and Computer Science · pp. 209–225 · Published 13 Apr 2013

10.9734/BJMCS/2013/2182

Abstract

In this paper, autoregressive integrated moving average (ARIMA) model is used to predict the prevalence and incidence of measles in the Ashanti Region of Ghana. The Mean Absolute Error (MAE) and the Mean Squared Error (MSE) are used to compare the in-sample forecasting performance of four selected competing models. The working data from the Ashanti Health Services spans from 2001 to 2011. It is evident from the analysis that measles data in the Ashanti Region of Ghana could best be modeled with ARIMA (2, 1, 1) and that measles prevalence in the Ashanti Region is expected to increase if no preventative measures are taken. The forecasting accuracy using MAE for ARIMA (2, 1, 1) is calculated as 28.1141 and the forecasting accuracy using MSE for ARIMA (2, 1, 1) is calculated as 2947.15.

Measles prevalence models forecasting autoregressive virus vaccine symptoms

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Cited by 4

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Peters Nwagor, Joyce Adaobi Okoro, Precious Yomi Igwe · Faculty of Natural and Applied Sciences Journal of Mathematical and Statistical Computing · 2025

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