Developing Stochastic Model for Forecasting Malaria Cases

Getnet Yirsaw, Bantie; Ayalew, Salie

ISBN 10: 6202300701 / ISBN 13: 9786202300704

Published by Scholars' Press, 2017

About this Item

Seller Inventory # 370157809

Bibliographic Details

• Title: Developing Stochastic Model for Forecasting Malaria Cases
• Publisher: Scholars' Press
• Publication Date: 2017
• Language: English
• ISBN 10: 6202300701
• ISBN 13: 9786202300704
• Binding: Soft cover
• Condition: New

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.