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 Deaths in Slovenia

Abstract

In this study, the ANN approach was applied to analyze COVID-19 deaths in Slovenia. This study is based on daily COVID-19 deaths in Slovenia for the period 1 January 2020 – 20 April 2021. The out-of-sample forecast covers 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 deaths in Slovenia are likely to continue to rise up to an equilibrium level of 53 deaths per day over the out-of-sample period. Amongst other suggested policy directions, there is need for the government of Slovenia to ensure adherence to safety guidelines while continuing to create awareness about the COVID-19 pandemic and to expand the vaccination programme.

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. 800-805

doi.org/10.47001/IRJIET/2021.506140

Full Paper
Download

References

  1. Aikins J S., Kunz J C., Shortliffe EH (1983) PUFF: an expert system for interpretation of pulmonary function data. Comput Biomed Res 1983; 16: 199–208.
  2. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  3. Khemasuwan D., Sorensen J S., & Colt HG (2020). Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. Eur Respir. 29: 200181 [https://doi.org/ 10.1183/16000617.0181-2020].
  4. KishanMehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks
  5. 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
  6. Pan J.,& Tompkins W J (1985). A real-time QRS detection algorithm. IEEE Trans Biomed Eng.  32: 230–236.
  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. Snow M G., Fallat R J., & Tyler WR (1988). Pulmonary consult: concept to application of an expert system. J Clin Eng 1988; 13: 201–206. 13 Grosan C, Abraham A. Rule-based expert systems. In: Intelligent Systems. Intelligent Systems Reference Library, Vol 17. Berlin, Heidelberg, Springer. 2011; pp. 149–185
  9. Weng S F., Reps J., Kai J., Garibaldi J M., & 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

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies