Forecasting Covid-19 New Cases in Seychelles

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: 527-532

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 09-07-2021

doi Logo doi.org/10.47001/IRJIET/2021.506092

Abstract
In this research paper, the ANN model was applied to forecast COVID-19 confirmed cases in Seychelles. This study is based on monthly new cases of COVID-19 in Seychelles 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 model indicate that the model is stable and adequate in forecasting daily confirmed cases of COVID-19 in the country. The results of the study indicate that that daily COVID-19 cases in Seychelles are likely to remain high, although characterized by recurrent downward trends over the out-of-sample period. We encourage relevant authorities to continue to implement preventive and control measures such as wearing of masks, banning of unnecessary travel, social distancing, and proper washing of hands and vaccinations.
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 Seychelles” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 6, pp 527-532, June 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.506092

References
  1. Johns Hopkins University (2020). Coronavirus, Coronavirus Research Center, Johns Hopkins University.
  2. Varela-Santos, S., & Melin, P. (2020). A New Approach for Classifying Coronavirus COVID-19 Based on its Manifestation on Chest X-rays Using Texture Features and Neural Networks, Information Sciences, 545 (2021): 403 – 414.
  3. Zeroual, A., et al. (2020). Deep Learning Methods for Forecasting COVID-19 Time Series Data: A Comparative Study, Chaos, Solitons and Fractals, 140 (2020): 1 – 12.
  4. Zhang, C., et al. (2020a). Measuring Imported Case Risk of COVID-19 From Inbound International Flights – A Case Study on China, University of International Business and Economics, China.
  5. Zhang, C., et al. (2020b). Exploring the Roles of High-speed Train, Air and Couch Services in the Spread of COVID-19 in China, Transport Politics, 94: 34 – 42.