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 Covid-19 Mortality in Ethiopia

Dr. Smartson. P. NYONIZICHIRe Project, University of Zimbabwe, Harare, ZimbabweMr. Thabani NYONISAGIT Innovation Center, Harare, ZimbabweMr. Tatenda. A. CHIHOHOIndependent Health Economist, Harare, Zimbabwe

Vol 5 No 6 (2021): Volume 5, Issue 6, June 2021 | Pages: 176-182

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 08-07-2021

doi Logo doi.org/10.47001/IRJIET/2021.506033

pdf iconFull Text PDF
Abstract
In this study, the ANN approach was applied to analyze COVID-19 deaths in Ethiopia. The employed data covers the period1 January 2020 to 20 April 2021 and the out-of-sample period ranges over the period21 April to 31 August 2021. The residuals and forecast evaluation criteria (Error, MSE and MAE) of the applied model indicate that the model is quite stable. The results of the study indicate that daily COVID-19 deaths in Ethiopia are likely to be between 0-50 deaths per day over the out-of-sample period. Therefore there is need for the government of Ethiopia to ensure adherence to safety guidelines while continuing to create awareness about the COVID-19 pandemic and COVID-19 vaccination. 
Keywords

ANN, COVID-19, Forecasting


Citation of this Article

Dr. Smartson. P. NYONI, Mr. Thabani NYONI, Mr. Tatenda. A. CHIHOHO, “Forecasting Covid-19 Mortality in Ethiopia” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 6, pp 176-182, June 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.506033

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Fojnica, A., Osmanoviae & Badnjeviae A (2016). Dynamic model of tuberculosis-multiple strain prediction based on artificial neural network. In proceedings of the 2016 5th Mediterranean conference on embedded computing pp290-293.
  2. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  3. Makoni M (2020). COVID-19 in Africa: half a year later. Lancet Infect Dis. 2020; 20(10):1127. https://doi.org/10.1016/S1473-3099(20)30708-8.
  4. Maradze, T. C., Nyoni, S. P., & Nyoni, T (2021). Modeling and Forecasting COVID-19 mortalities in the United States of America using artificial neural networks (ANN). International Journal of innovations in Engineering and Technology (IRJIET), 5 (3):533-539
  5. Maradze, T. C., Nyoni, S. P., & Nyoni, T (2021). Modeling and Forecasting Child immunization against measles disease in Djibouti using artificial neural networks (ANNs). International Journal of innovations in Engineering and Technology (IRJIET), 5 (3):449-452.
  6. Naizhuo Zhao., Katia Charland., Mabel Carabali., Elaine O., Nsoesie., Mathieu MaheuGiroux., Erin Rees., Mengru Yuan., Cesar Garcia Balaguera., Gloria Jaramillo Ramirez., & Kate Zinszer (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
  7. Nyoni, S. P., & Nyoni, T (2021). Forecasting ART coverage in Egypt using artificial neural networks. International Journal of Innovations in Engineering and Technology (IRJIET), 5 (3): 161-165.
  8. Nyoni, S. P., & Nyoni, T (2021). Forecasting ART coverage in Kenya using the Multilayer perceptron neural network. International Journal of Innovations in Engineering and Technology (IRJIET), 5 (3): 161-165.
  9. Nyoni, S. P., & Nyoni, T (2021). Forecasting ART coverage in Malawi using the Multilayer perceptron neural network. International Journal of Innovations in Engineering and Technology (IRJIET), 5 (3): 222-226
  10. Smartson P. Nyoni., Thabani Nyoni & Tatenda. A. Chihoho (2020). Prediction of daily new COVID-19 cases in Italy using artificial neural networks. IJARIIE-ISSN(O)-2395-4396 Vol-6 Issue-6 2020,
  11. Smartson. P. Nyoni, Thabani Nyoni, Tatenda. A. Chihoho (2020) Prediction of new Covid-19 cases in Ghana using artificial neural networks. IJARIIE Vol-6 Issue-6             2395-4396
  12. Smartson. P. Nyoni., Thabani Nyoni & Tatenda A. Chihoho (2020). Forecasting COVID-19 cases in Ethiopia using artificial neural networks, IJARIIE, 6, 6, 2395-4396
  13. Smartson. P. Nyoni., Thabani Nyoni., Tatenda. A. Chihoho (2020). Prediction of daily new Covid-19 cases in Egypt using artificial neural networks. IJARIIE-  Vol-6 Issue-6         2395-4396
  14. World Health Organization (2020). Covid-19 Response Bullettin Ethiopia. https://www.afro.who.int/sites/default/files/2020-04/ETHIOPIA_COVID-1 9%20response%20bulletin_10APR2020%20%28002%29.pdf (Accessed 8 Jan 2021)
  15. World Health Organization (2020). WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/WHO-COVID-19-global-data.csv
  16. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50: 159–175.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.