Forecasting Covid-19 New Cases in South Sudan

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: 533-538

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/2021.506093

Abstract
When it comes to public health these days, COVID-19 is of serious concern and considered as the supreme crisis of the present era. A surge in the number patients testing positive for COVID-19 has created a lot of stress and frustration on governing bodies worldwide and they are finding it difficult to tackle the situation. In this research article, the ANN approach was applied to analyze COVID-19 case volumes in South Sudan. This study is based on daily new cases of COVID-19 in South Sudan 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 reveal that the model is stable in forecasting COVID-19 cases in South Sudan. It is projected that daily COVID-19 cases in South Sudan are likely to decline to zero cases per day around early April 2021. The government of South Sudan should continue to ensure that there is compliance to control and preventive COVID-19 measures such as social distancing, quarantine, isolation, face-mask wearing and so on. There is also need to embrace the vaccination programme in the country.
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 South Sudan” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 6, pp 533-538, June 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.506093

References
  1. Alazab, M., et al. (2020). COVID-19 Prediction and Detection Using Deep Learning, International Journal of Computer Information Systems and Industrial Management Applications, 12: 168 – 181.
  2. Butt, C., et al. (2020). Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia, Applied Intelligence, pp: 1 – 7.
  3. Chen, N., et al. (2020). Epidemiological and Clinical Characteristics of 99 Cases of 2019 Novel Coronavirus Pneumonia in Wuhan, China: A Descriptive Study, Lancet, 395: 507 – 513.
  4. Lei, Q., et al. (2020). Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index, International Journal of Environmental Research and Public Health, 17 (2365): 1 – 14.
  5. McCloskey, B., et al. (2020). Mass Gathering Events and Reducing Further Spread of COVID-19: A Political and Public Health Dilemma, Lancet, 395: 1096 – 1099.
  6. Medina-Mendieta, J. F., et al. (2020). COVID-19 Forecasts for Cuba Using Logistic Regression and Gompertz Curves, MEDICC, 22 (3): 32 – 39.
  7. Remuzzi, A., & Remuzzi, G. (2020). COVID-19 and Italy: What Next? Lancet, pp: 1 – 13.
  8. WHO (2020). Laboratory Testing for Coronavirus Disease 2019 (COVID-19) in Suspected Human Cases: Interim Guidance, WHO, Geneva.