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.
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 2, Issue 1) |
| DOI | 10.11648/j.sjams.20140201.15 |
| Page(s) | 31-41 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
ARIMA Model, SARIMA Model, Forecasting, ARMA Model, Box-Jenkins Methods, Malaria Mortality, Akaike Information Criteria, Bayesian Information Criterion
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APA Style
Ekezie Dan Dan, Opara Jude, Okenwe Idochi. (2014). 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, 2(1), 31-41. https://doi.org/10.11648/j.sjams.20140201.15
ACS Style
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). Sci. J. Appl. Math. Stat. 2014, 2(1), 31-41. doi: 10.11648/j.sjams.20140201.15
AMA Style
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). Sci J Appl Math Stat. 2014;2(1):31-41. doi: 10.11648/j.sjams.20140201.15
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Download@article{10.11648/j.sjams.20140201.15,
author = {Ekezie Dan Dan and Opara Jude and Okenwe Idochi},
title = {Modelling and Forecasting Malaria Mortality Rate using SARIMA Models (A Case Study of Aboh Mbaise General Hospital, Imo State Nigeria)},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {2},
number = {1},
pages = {31-41},
doi = {10.11648/j.sjams.20140201.15},
url = {https://doi.org/10.11648/j.sjams.20140201.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20140201.15},
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.},
year = {2014}
}
TY - JOUR T1 - Modelling and Forecasting Malaria Mortality Rate using SARIMA Models (A Case Study of Aboh Mbaise General Hospital, Imo State Nigeria) AU - Ekezie Dan Dan AU - Opara Jude AU - Okenwe Idochi Y1 - 2014/03/20 PY - 2014 N1 - https://doi.org/10.11648/j.sjams.20140201.15 DO - 10.11648/j.sjams.20140201.15 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 31 EP - 41 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20140201.15 AB - 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. VL - 2 IS - 1 ER -
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