International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Forecasting the Future Trends of Under Five Mortality Rate for Cuba Using Double Exponential Smoothing Model

Abstract

This study uses annual time series data on under five mortality rate (U5MR) for Cuba from 1960 to 2020 to predict future trends of U5MR over the period 2021 to 2030. Residuals and forecast evaluation statistics indicate that the applied model is stable in forecasting U5MR in Cuba. Holt’s linear (double exponential smoothing) model was applied in this study. The optimal values of smoothing constants α and β are 0.9 and 0.1 respectively based on minimum MSE. The results of the study showed that annual U5MR will continue to decline throughout the out of sample period. Therefore, we implore the Cuban government to continue supporting maternal and child health program activities in order to maintain under five mortality below 25 deaths per 1000 live births. 

Country : Zimbabwe

1 Dr. Smartson. P. NYONI2 Thabani NYONI

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. Independent Researcher & Health Economist, Harare, Zimbabwe

IRJIET, Volume 6, Issue 7, July 2022 pp. 200-204

doi.org/10.47001/IRJIET/2022.607041

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References

  1. Box, D. E., and Jenkins, G. M. (1970). Time Series Analysis, Forecasting and Control, Holden Day, London.
  2. Nyoni, T. (2018). Box-Jenkins ARIMA Approach to Predicting net FDI Inflows in Zimbabwe, University Library of Munich, MPRA Paper No. 87737.
  3. Eva Ostertagová1and Oskar Ostertag (2011). Modelling of Mechanical and Mechatronic systems 2011, The 4th International conference, pp. 380-384.
  4. MONTGOMERY, D. C., JOHNSON, L. A., GARDINER, J. S., Forecasting and Time Series Analysis, McGraw-Hill, Inc., 1990, ISBN 0-07-042858-1
  5. ACZEL, A. D., Complete Business Statistics, Irwin, 1989, ISBN 0-256-05710-8
  6. Zhou L., Zhao P., Wu D., Cheng C and Huang H (2018)Time series model for forecasting the number of new admission inpatients BMC Medical Informatics and Decision Making, 18, 39. https://doi.org/10.1186/s12911-018-0616-8
  7. Nyoni & Nyoni T (2019). Forecasting TB notifications at Silobela District Hospital, Zimbabwe, IJARIIE, 5,6, 2395-4396.
  8. Nyoni & Nyoni T (2019). Forecasting TB notifications at Zengeza clinic, Zimbabwe.Online at https://mpra.ub.uni-muenchen.de/97331/ MPRA Paper No. 97331.
  9. Zhao N., Charland K., Carabali M., Elaine O., Nsoesie.,MaheuGiroux M., Rees E., Yuan M., Balaguera C G., Ramirez G J., & Zinszer K (2020). Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0008056
  10. Panch T., Szolovits P., & Atun R (2018). Artificial intelligence, machine learning and health systems, 5, 2, 020303.

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