Forecasting Covid-19 Mortality in Saudi Arabia

Abstract

In this study, the ANN approach was applied to analyze COVID-19 mortality in Saudi Arabia. This study is based on daily COVID-19 deaths in Saudi Arabia 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 Saudi Arabia are likely to rise up to an equilibrium case volume of approximately 37 deaths per day over the out-of-sample period. Therefore there is need for the government of Saudi Arabia 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. 788-793

doi.org/10.47001/IRJIET/2021.506138

References

  1. Bashar Moneer Yahya & Farah Samier Yahya & Rayan Ghazi Thannoun (2021). COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq, Applied Geomatics https://doi.org/10.1007/s12518-021-00365-4
  2. He J., Baxter SL., Xu J., Xu J., Zhou X., & Zhang K (2019). The practical implementation of artificial intelligence technologies in medicine. Nat Med 25(1):30–36. https://doi.org/10.1038/s41591-018-0307-0
  3. Lai CC., Shih TP., Ko WC., Tang HJ, Hsueh PR (2020) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int J Antimicrob Agents 55:105924. https://doi.org/10.1016/j. ijantimicag.2020.105924
  4. Steiner MC, Gibson KM, Crandall KA (2020) Drug resistance prediction using deep learning techniques on HIV-1 sequence data. Viruses 12(5):560. https://doi.org/10.3390/v12050560
  5. Wang P., Yao J., Wang G., Hao F., Shrestha S., Xue B., and Peng Y (2019) Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants. Science of the Total Environment, 693.