Forecasting Covid-19 New Cases in Liechtenstein

Abstract

In this study, the ANN approach was applied to analyze COVID-19 new cases in Liechtenstein. The employed data covers the period 1 January 2020 – 25 March 2021 and the out-of-sample period ranges over the period 26 March – 31 July 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 cases in Liechtenstein are likely to rise up to around 55 cases per day over the out-of-sample period. Amongst other suggested policy directions, there is need for the government of Liechtenstein to ensure adherence to safety guidelines while continuing to create awareness about the COVID-19 pandemic.

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. 298-303

doi.org/10.47001/IRJIET/2021.506053

References

  1. CDC (2020). The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. China CDC Weekly 2, 113–122.
  2. F. Rustam., A. MEHMOOD., A. RESHI., S. ULLAH., B.-W. ON., W. ASLAM and G. S. CHOI(2020), "COVID-19 Future Forecasting Using Supervised Machine Learning Models," IEEE Access , vol. 8, pp. 101489 - 101499.
  3. 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
  4. N. Zheng., S. Du., J. Wang., H. Zhang., W. Cui., T. Yang., B. Lou., Y. Chi., H. Long., M. Ma., Q. Yuan & S. Zhang (2020). "Predicting COVID-19 in China Using Hybrid AI Model," IEEE Trans Cybern.
  5. 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.
  6. Tamer Sh. Mazen (2020). A Novel Machine Learning based Model for COVID-19 Prediction, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 11, 2020
  7. Thomas Gerhard Wolf., Oliver Zeyer and Guglielmo Campus (2020), COVID-19 in Switzerland and Liechtenstein: A Cross-Sectional Survey among Dentists’ Awareness, Protective Measures and Economic Effects, Int. J. Environ. Res. Public Health 2020, 17, 9051; doi:10.3390/ijerph17239051
  8. World Health Organization (2020). Coronavirus Disease (COVID-19) Weekly Epidemiological Update and Weekly Operational Update. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/ situation-reports.
  9. World Health Organization (2020). Rolling Updates on Coronavirus Disease (COVID-19). Available online: https: //www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline.
  10. Yan, L., Zhang, H., Goncalves, J (2020). "An interpretable mortality prediction model for COVID-19 patients," Nature Machine Intelligence, vol. 2, pp. 283-288.