Forecasting Covid-19 New Cases in Mauritania

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: 407-412

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/2021.506071

Abstract
In this study, the ANN approach was applied to analyze COVID-19 new cases in Mauritania. 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 Mauritania are likely to remain high over the out-of-sample period. Amongst other suggested policy directions, there is need for the government of Mauritania to ensure adherence to safety guidelines while continuing to create awareness about the COVID-19 pandemic.
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 Mauritania” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 6, pp 407-412, June 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.506071

References
  1. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  2. Fojnica, A., Osmanoviae & Badnjeviae A (2016). Dynamic model of tuberculosis-multiple strain prediction based on artificial neural network. In proceedings of the 2016 5th Mediterranean conference on embedded computing pp290-293
  3. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  4. Kishan Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks
  5. Naizhuo Zhao., Katia Charland., Mabel Carabali., Elaine O., Nsoesie., Mathieu MaheuGiroux., Erin Rees., Mengru Yuan., Cesar Garcia Balaguera., Gloria Jaramillo Ramirez., & Kate Zinszer (2020). Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0008056
  6. Smartson. P. Nyoni., Thabani Nyoni., Tatenda. A. Chihoho (2020). Prediction of daily new Covid-19 cases in Egypt using artificial neural networks. IJARIIE-  Vol-6 Issue-6         2395-4396
  7. UNDP (2020). Support to the National Response to Contain the Impact of COVID-19, pp 1-2.
  8. UNICEF (2020) MAURITANIA: COVID-19 Situation Report – #14.
  9. Zhang G P, “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.