Developing Stochastic Model for Forecasting Malaria Cases

Bantie Getnet Yirsaw

ISBN 10: 6202300701 / ISBN 13: 9786202300704

Published by Scholars' Press Aug 2017, 2017

About this Item

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.

Seller Inventory # 9786202300704

Bibliographic Details

• Title: Developing Stochastic Model for Forecasting Malaria Cases
• Publisher: Scholars' Press Aug 2017
• Publication Date: 2017
• Language: English
• ISBN 10: 6202300701
• ISBN 13: 9786202300704
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 143 grams

About this title

Synopsis

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.

About the Author

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.