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 Belgium

Abstract

In this study, the ANN approach was applied to analyze COVID-19 mortality in Belgium. The employed data covers the period 1 January 2020-20 April 2021 and the out-of-sample period ranges over the period 21 April-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 mortality cases in Belgium are likely to decline significantly over the out-of-sample period. There is need, however, for the government of Belgium to ensure adherence to safety guidelines while continuing to create awareness about the COVID-19 pandemic and scaling up COVID-19 vaccination.

Country : Zimbabwe

1 Dr. Smartson. P. NYONI2 Mr. Thabani NYONI3 Mr. Tatenda. A. CHIHOHO

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. SAGIT Innovation Center, Harare, Zimbabwe
  3. Independent Health Economist, Harare, Zimbabwe

IRJIET, Volume 5, Issue 6, June 2021 pp. 710-713

doi.org/10.47001/IRJIET/2021.506124

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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. 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
  4. Niazkar M (2019). Revisiting the estimation of Colebrook friction factor: a comparison between artificial intelligence models and CW based explicit equations. KSCE J Civ Eng. 2019; 23(10):4311–26. https://doi.org/10.1007/ s12205-019-2217-1.
  5. Niazkar M (2020). Assessment of artificial intelligence models for calculating optimum properties of lined channels. J Hydroinf. https://doi.org/10. 2166/hydro.2020.050.
  6. Niazkar M., Talebbeydokhti N., & Afzali S-H (2020). Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations. Scientia Iranica. https://doi.org/10.24200/SCI.2020.51432.2175.
  7. 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
  8. Smartson. P. Nyoni., Thabani Nyoni & Tatenda A. Chihoho (2020). Forecasting COVID-19 cases in Ethiopia using artificial neural networks, IJARIIE, 6, 6, 2395-4396
  9. 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
  10. Zhang G P (2003). “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.

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