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Sinopsis
Malaria is a serious public health problem in developing countries like Ethiopia. Early prediction of malaria cases is very important for its control and intervention. This work developed stochastic model for forecasting malaria cases in Addis Zemen, South Gondar, Ethiopia. Data of monthly malaria cases from January 2007 to June 2016 were obtained from Addis Zemen health center, south Gondar, Ethiopia. The autoregressive integrated moving average (ARIMA) model, is typically applied to forecast the malaria cases; it can take into account changing trends, seasonal variation, and random disturbances in time series. Generalized Autoregressive conditional heteroscedasticity (GARCH) models are the prevalent tools used to deal with time series heteroscedasticity. Although both two families of models could reasonably forecast the malaria cases, the GARCH model demonstrated better goodness-of-fit than the SARIMA model. The seasonal trend of malaria cases is predicted to have lower monthly malaria cases in January and higher malaria cases in October. This work is the first study to establish the ARIMA model and GARCH model for forecasting the monthly malaria cases in Addis-Zemen.
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Acerca del autor
Bantie Getnet Yirsaw (author) holds Bachelor of Science Degree in Statistics and Masters of Science in Bio-Statistics from University of Gondar, Ethiopia. He is a lecturer at Woldia University, Ethiopia. Dr. Salie Ayalew (co-author) is Assistant professor of Statistics at University of Gondar, Ethiopia.
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Developing Stochastic Model for Forecasting Malaria Cases
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Developing Stochastic Model for Forecasting Malaria Cases
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Developing Stochastic Model for Forecasting Malaria Cases
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Developing Stochastic Model for Forecasting Malaria Cases
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Scholars' Press Aug 2017, 2017
ISBN 10: 6202300701
ISBN 13: 9786202300704
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Malaria is a serious public health problem in developing countries like Ethiopia. Early prediction of malaria cases is very important for its control and intervention. This work developed stochastic model for forecasting malaria cases in Addis Zemen, South Gondar, Ethiopia. Data of monthly malaria cases from January 2007 to June 2016 were obtained from Addis Zemen health center, south Gondar, Ethiopia. The autoregressive integrated moving average (ARIMA) model, is typically applied to forecast the malaria cases; it can take into account changing trends, seasonal variation, and random disturbances in time series. Generalized Autoregressive conditional heteroscedasticity (GARCH) models are the prevalent tools used to deal with time series heteroscedasticity. Although both two families of models could reasonably forecast the malaria cases, the GARCH model demonstrated better goodness-of-fit than the SARIMA model. The seasonal trend of malaria cases is predicted to have lower monthly malaria cases in January and higher malaria cases in October. This work is the first study to establish the ARIMA model and GARCH model for forecasting the monthly malaria cases in Addis-Zemen. 84 pp. Englisch. Nº de ref. del artículo: 9786202300704
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Developing Stochastic Model for Forecasting Malaria Cases
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Developing Stochastic Model for Forecasting Malaria Cases
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Developing Stochastic Model for Forecasting Malaria Cases
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Getnet Yirsaw BantieBantie Getnet Yirsaw (author) holds Bachelor of Science Degree in Statistics and Masters of Science in Bio-Statistics from University of Gondar, Ethiopia. He is a lecturer at Woldia University, Ethiopia. Dr. Salie. Nº de ref. del artículo: 167230366
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Developing Stochastic Model for Forecasting Malaria Cases
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Developing Stochastic Model for Forecasting Malaria Cases
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Malaria is a serious public health problem in developing countries like Ethiopia. Early prediction of malaria cases is very important for its control and intervention. This work developed stochastic model for forecasting malaria cases in Addis Zemen, South Gondar, Ethiopia. Data of monthly malaria cases from January 2007 to June 2016 were obtained from Addis Zemen health center, south Gondar, Ethiopia. The autoregressive integrated moving average (ARIMA) model, is typically applied to forecast the malaria cases; it can take into account changing trends, seasonal variation, and random disturbances in time series. Generalized Autoregressive conditional heteroscedasticity (GARCH) models are the prevalent tools used to deal with time series heteroscedasticity. Although both two families of models could reasonably forecast the malaria cases, the GARCH model demonstrated better goodness-of-fit than the SARIMA model. The seasonal trend of malaria cases is predicted to have lower monthly malaria cases in January and higher malaria cases in October. This work is the first study to establish the ARIMA model and GARCH model for forecasting the monthly malaria cases in Addis-Zemen. Nº de ref. del artículo: 9786202300704
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Developing Stochastic Model for Forecasting Malaria Cases
Publicado por
Scholars' Press Aug 2017, 2017
ISBN 10: 6202300701
ISBN 13: 9786202300704
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Malaria is a serious public health problem in developing countries like Ethiopia. Early prediction of malaria cases is very important for its control and intervention. This work developed stochastic model for forecasting malaria cases in Addis Zemen, South Gondar, Ethiopia. Data of monthly malaria cases from January 2007 to June 2016 were obtained from Addis Zemen health center, south Gondar, Ethiopia. The autoregressive integrated moving average (ARIMA) model, is typically applied to forecast the malaria cases; it can take into account changing trends, seasonal variation, and random disturbances in time series. Generalized Autoregressive conditional heteroscedasticity (GARCH) models are the prevalent tools used to deal with time series heteroscedasticity. Although both two families of models could reasonably forecast the malaria cases, the GARCH model demonstrated better goodness-of-fit than the SARIMA model. The seasonal trend of malaria cases is predicted to have lower monthly malaria cases in January and higher malaria cases in October. This work is the first study to establish the ARIMA model and GARCH model for forecasting the monthly malaria cases in Addis-Zemen.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 84 pp. Englisch. Nº de ref. del artículo: 9786202300704
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