Forecasting Covid-19 New Cases in Azerbaijan

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: 732-737

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 10-07-2021

doi Logo doi.org/10.47001/IRJIET/2021.506128

Abstract
During the current global urgency, scientists, clinicians and healthcare experts around the globe keep on searching for accurate and reliable COVID-19 forecasting models to support in tackling the deadly and highly infectious disease. In this research paper, the ANN approach was applied to analyze COVID-19 daily cases in Azerbaijan. This study is based on daily new cases of COVID-19 in Azerbaijan for the period 1 January 2020 – 25 March 2021. The out-of-sample forecast covers the period 26 March 2021 – 31 July 2021. The residuals and forecast evaluation criteria (Error, MSE and MAE) of the applied ANN (12, 12, 1) model indicate that the model is stable in forecasting daily COVID-19 cases in the country. The results of the study indicate that that daily COVID-19 cases in Azerbaijan are likely to remain significantly high over the out-of-sample period. The government of Azerbaijan, through its ministry of health, should continue to implement COVID-19 control and prevention measures such as isolation, quarantine, testing and tracing, face-mask wearing, sanitization of hands and so on, in line with WHO standards.
Keywords

ANN, COVID-19, Forecasting, Zimbabwe, corona, pandemic


Citation of this Article

Dr. Smartson. P. NYONI, Mr. Thabani NYONI, Mr. Tatenda. A. CHIHOHO, “Forecasting Covid-19 New Cases in Azerbaijan” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 6, pp 732-737, June 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.506128

References
  1. Alakus, T. B., & Turkoglu, I. (2020). Comparison of Deep Learning Approaches to Predict COVID-19 Infection, Chaos, Solitons and Fractals, 140 (2020): 1 – 7.
  2. Huang, C., et al. (2020). Clinical Features of Patients Infected With 2019 Novel Coronavirus in Wuhan, China, Lancet, 395: 497 – 506.
  3. Wang, D., et al. (2020). Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-infected Pneumonia in Wuhan, China, J. Am. Med. Assoc., 323 (11): 1 – 6.
  4. WHO (2020). Health Topics: Coronavirus, WHO, Geneva.