Comparative Analysis of Sarima and Setar Models in Predicting Pneumonia Cases in Kenya
International Journal of Data Science and Analysis
Volume 6, Issue 1, February 2020, Pages: 48-57
Received: Feb. 24, 2020; Accepted: Mar. 6, 2020; Published: Mar. 18, 2020
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Fredrick Agwata Nyamato, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Thomas Mageto, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
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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.
Seasonal Autoregressive Integrated Moving Average, Self-excited Threshold Autoregressive, Stationarity and Linearity
To cite this article
Fredrick Agwata Nyamato, Anthony Wanjoya, Thomas Mageto, Comparative Analysis of Sarima and Setar Models in Predicting Pneumonia Cases in Kenya, International Journal of Data Science and Analysis. Vol. 6, No. 1, 2020, pp. 48-57. doi: 10.11648/j.ijdsa.20200601.16
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