American Journal of Mathematics and Statistics
pISSN: 2162948X eISSN: 21628475
2019; 9(1): 1116
doi:10.5923/j.ajms.20190901.02
Obubu Maxwell^{1}, Babalola A. Mayowa^{2}, Ikediuwa U. Chinedu^{1}, Amadi E. Peace^{3}
^{1}Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
^{2}Department of Statistics, University of Ilorin, Ilorin, Nigeria
^{3}Department of Statistics, Abia State Polytechnic, Aba, Nigeria
Correspondence to: Obubu Maxwell, Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria.
Email: 
Copyright © 2019 The Author(s). Published by Scientific & Academic Publishing.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Malaria is an urgent public health priority. Malaria and the costs of treatment trap families in a cycle of illness, suffering and poverty. Today, half of the world population is at risk. The study intended mainly to model and forecast the malaria mortality rate for the coming years. The BoxJenkins Autoregressive Integrated Moving Average (ARIMA) was employed, parameters were estimated and several diagnostic tests were performed. Series of tentative models were developed to forecast the mortality rate based on minimum AIC and BIC values. Results: ARIMA (0,1,0) model was proved to be the best model for forecasting after satisfying the model assumptions. The forecasted results revealed a decreasing pattern of malaria mortality rate 2016 to 2022. Malaria Mortality was found to be on a decrease in the forecasted period. However, in order to zero mortality due to malaria from our society, government and health experts still need to put hands together to sanitize the system in terms of drugs manufacturing.
Keywords: Malaria mortality, ARIMA models, Augmented dickeyfuller test, ACF/PACF plots, Forecasting, Box and Jenkins
Cite this paper: Obubu Maxwell, Babalola A. Mayowa, Ikediuwa U. Chinedu, Amadi E. Peace, Biometry Investigation of Malaria Disease, Mortality and Modelling; an Autoregressive Integrated Approach, American Journal of Mathematics and Statistics, Vol. 9 No. 1, 2019, pp. 1116. doi: 10.5923/j.ajms.20190901.02.
Figure 1. The Graph Above is the Time Series plot for Malaria Mortality data Series 

Figure 2. The graph above shows the Time Series plot for differenced Malaria Mortality data series 

Figure 3. The figure presents the Correlogram of residuals for Malaria Mortality 


Figure 4. The table presents the Correlogram of residuals for Malaria Mortality 