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.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Mr. Thabani NYONI3 Mr. Tatenda. A. CHIHOHO
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Dan
W. Patterson (1995) Artificial Neural networks Theory and Applications.
Singapore; New York: Prentice Hall.
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
Kaushik
AC & Sahi. S (2018). Artificial neural network-based model for orphan
GPCRs.Neural.Comput.Appl. 29,985-992
Kishan
Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of
artificial neural networks
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
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
UNDP
(2020). Support to the National Response to Contain the Impact of COVID-19, pp
1-2.
