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 Daily Covid-19 Deaths in Germany Using Artificial Neural Networks

Dr. Smartson. P. NYONIZICHIRe Project, University of Zimbabwe, Harare, ZimbabweThabani NYONISAGIT Innovation Centre, Harare, ZimbabweTatenda. A. CHIHOHOIndependent Researcher, Harare, Zimbabwe

Vol 5 No 3 (2021): Volume 5, Issue 3, March 2021 | Pages: 400-406

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 01-04-2021

doi Logo doi.org/10.47001/IRJIET/2021.503069

pdf iconFull Text PDF
Abstract
In this research paper, the ANN approach was applied to analyze daily COVID-19 deaths in Germany. The employed daily data covers the period to 1 January 2020 to 31 December 2020 and the out-of-sample period ranges over the period to 1January 2021 to 31 May 2021. The residuals and forecast evaluation criteria (Error, MSE and MAE) of the applied model indicate that the model is stable in forecasting daily COVID-19 cases in Germany. The applied ANN (12, 12, 1) model projections indicate thatCOVID-19 mortality in Germany will generally range between 29 and 1000 deaths per day over the out-of-sample period. Therefore the authorities in Germany are encouraged to continue applying WHO guidelines on prevention and control of COVID-19 including vaccination of its population in order to achieve herd immunity. 
Keywords

ANN, Forecasting, COVID-19.


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, Tatenda. A. CHIHOHO, “Prediction of Confirmed Daily Covid-19 Cases in Mozambique Using Artificial Neural Networks” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 3, pp 400-406, March 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.503069

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. Bai, S., Kolter, J.Z & Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. Cornell University Library, arXiv.org, ISSN: 2331-8422, arXiv: 1409.0473.
  2. Crescenzio Gallo (2015). Artificial neural networks tutorial, Encyclopedia of information science and Technology, 3rd Edition, pp 2-13.
  3. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.   
  4. 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.
  5. Gomes, G. S. S., et al. (2011). Comparison of New Activation Functions in Neural Network for Forecasting Financial Time Series, Neural Computing & Applications, 20: 417 – 439.
  6. Kaushik AC &Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992.
  7. KishanMehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks. Cambridge, Mass.: MIT Press.
  8. Smartson P Nyoni., Thabani Nyoni & Tatenda A Chihoho (2020) Prediction of new Covid-19 cases in Spain using artificial neural networks. IJARIIE Vol-6 Issue-6             2395-4396.
  9. 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.
  10. Smartson. P. Nyoni., Thabani Nyoni & Tatenda A. Chihoho (2020). Forecasting COVID-19 cases in Zimbabwe using artificial neural networks, IJARIIE, 6, 6, 2395-4396.
  11. Smartson. P. Nyoni., Thabani Nyoni & Tatenda A. Chihoho (2020). Forecasting COVID-19 cases in Ethiopia using artificial neural networks, IJARIIE, 6, 6, 2395-4396.
  12. 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.
  13. Weng SF., Reps J., Kai J., Garibaldi JM & Qureshi N (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE 12(4): e0174944. https://doi.org/10.1371/journal.pone.0174944
  14. Yan, H., Jiang, J., Zheng, J., Peng, C & Li, Q. (2006) A multilayer perceptron based medical decision support system for heart disease diagnosis. Expert Syst. Appl. 2006, 30, 272–281.
  15. Zhang G P (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.