Kenya is a country located in Eastern part of Africa with approximate population of 46.5 million, with majority of the population constituting youths under the age of 35 years. The country has experienced increased morbidity rate arising from Pneumonia disease like other countries all over the world. As per recent studies 2 million children lose lives from pneumonia disease [1]. This study applies two models, one is linear model Seasonal autoregressive model (SARIMA) and another is a non-linear model called self-Excited Threshold Autoregressive (SETAR) in projection of cases in Kenya. Data for usage for purpose of this study was obtained Ministry of Health of Kenya of a period of 20 years from January 1999 to December 2018. The data collected is seasonal the number of case from period to period depending on climatic condition. Although both models performs well in pneumonia projection, non-linear SETAR models outperforms linear SARIMA. By carrying out a comparative analysis by use of Diebold-Mariano test, which revealed that there were no significant difference in the forecasting performance of the two models. The best model identified between the two models i.e. SETAR which best fit the data, can be applied in predicting pneumonia cases beyond the period under consideration. Other studies can be carried to come up with a model for every specific region in the country, to assist in resources allocation to specific parts of the country.
| Published in | International Journal of Data Science and Analysis (Volume 6, Issue 1) |
| DOI | 10.11648/j.ijdsa.20200601.16 |
| Page(s) | 48-57 |
| 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. |
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Copyright © The Author(s), 2020. Published by Science Publishing Group |
Seasonal Autoregressive Integrated Moving Average, Self-excited Threshold Autoregressive, Stationarity and Linearity
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APA Style
Fredrick Agwata Nyamato, Anthony Wanjoya, Thomas Mageto. (2020). Comparative Analysis of Sarima and Setar Models in Predicting Pneumonia Cases in Kenya. International Journal of Data Science and Analysis, 6(1), 48-57. https://doi.org/10.11648/j.ijdsa.20200601.16
ACS Style
Fredrick Agwata Nyamato; Anthony Wanjoya; Thomas Mageto. Comparative Analysis of Sarima and Setar Models in Predicting Pneumonia Cases in Kenya. Int. J. Data Sci. Anal. 2020, 6(1), 48-57. doi: 10.11648/j.ijdsa.20200601.16
AMA Style
Fredrick Agwata Nyamato, Anthony Wanjoya, Thomas Mageto. Comparative Analysis of Sarima and Setar Models in Predicting Pneumonia Cases in Kenya. Int J Data Sci Anal. 2020;6(1):48-57. doi: 10.11648/j.ijdsa.20200601.16
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Download@article{10.11648/j.ijdsa.20200601.16,
author = {Fredrick Agwata Nyamato and Anthony Wanjoya and Thomas Mageto},
title = {Comparative Analysis of Sarima and Setar Models in Predicting Pneumonia Cases in Kenya},
journal = {International Journal of Data Science and Analysis},
volume = {6},
number = {1},
pages = {48-57},
doi = {10.11648/j.ijdsa.20200601.16},
url = {https://doi.org/10.11648/j.ijdsa.20200601.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200601.16},
abstract = {Kenya is a country located in Eastern part of Africa with approximate population of 46.5 million, with majority of the population constituting youths under the age of 35 years. The country has experienced increased morbidity rate arising from Pneumonia disease like other countries all over the world. As per recent studies 2 million children lose lives from pneumonia disease [1]. This study applies two models, one is linear model Seasonal autoregressive model (SARIMA) and another is a non-linear model called self-Excited Threshold Autoregressive (SETAR) in projection of cases in Kenya. Data for usage for purpose of this study was obtained Ministry of Health of Kenya of a period of 20 years from January 1999 to December 2018. The data collected is seasonal the number of case from period to period depending on climatic condition. Although both models performs well in pneumonia projection, non-linear SETAR models outperforms linear SARIMA. By carrying out a comparative analysis by use of Diebold-Mariano test, which revealed that there were no significant difference in the forecasting performance of the two models. The best model identified between the two models i.e. SETAR which best fit the data, can be applied in predicting pneumonia cases beyond the period under consideration. Other studies can be carried to come up with a model for every specific region in the country, to assist in resources allocation to specific parts of the country.},
year = {2020}
}
TY - JOUR T1 - Comparative Analysis of Sarima and Setar Models in Predicting Pneumonia Cases in Kenya AU - Fredrick Agwata Nyamato AU - Anthony Wanjoya AU - Thomas Mageto Y1 - 2020/03/18 PY - 2020 N1 - https://doi.org/10.11648/j.ijdsa.20200601.16 DO - 10.11648/j.ijdsa.20200601.16 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 48 EP - 57 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20200601.16 AB - Kenya is a country located in Eastern part of Africa with approximate population of 46.5 million, with majority of the population constituting youths under the age of 35 years. The country has experienced increased morbidity rate arising from Pneumonia disease like other countries all over the world. As per recent studies 2 million children lose lives from pneumonia disease [1]. This study applies two models, one is linear model Seasonal autoregressive model (SARIMA) and another is a non-linear model called self-Excited Threshold Autoregressive (SETAR) in projection of cases in Kenya. Data for usage for purpose of this study was obtained Ministry of Health of Kenya of a period of 20 years from January 1999 to December 2018. The data collected is seasonal the number of case from period to period depending on climatic condition. Although both models performs well in pneumonia projection, non-linear SETAR models outperforms linear SARIMA. By carrying out a comparative analysis by use of Diebold-Mariano test, which revealed that there were no significant difference in the forecasting performance of the two models. The best model identified between the two models i.e. SETAR which best fit the data, can be applied in predicting pneumonia cases beyond the period under consideration. Other studies can be carried to come up with a model for every specific region in the country, to assist in resources allocation to specific parts of the country. VL - 6 IS - 1 ER -
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