Forecasting Confirmed Cases of Covid-19 in Namibia Using Artificial Neural Networks
Abstract
In this research article, the ANN approach was applied to analyze daily
COVID-19 cases in Namibia. The employed data covers the period 1 January 2020
to 31 December 2020 and the out-of-sample period ranges over the period 1
January 2021 to 31 May 2021. The residuals and forecast evaluation criteria
(Error, MSE and MAE) of the applied ANN (12, 12, 1) model indicate that the
model i daily COVID-19 cases is stable in forecasting daily new COVID-19 cases
in Namibia. The model predictions suggest that daily COVID-19 cases are likely
to be between 0-800 cases per day over the period 1 January 2021 to 31 May
2021. Therefore the government should continue enforcing adherence to WHO
guidelines on prevention and control of COVID-19.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Thabani NYONI3 Tatenda. A. CHIHOHO
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Bai, S., Kolter, J.Z & Koltun,
V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent
Networks for Sequence Modeling. Cornell University Library, arXiv.org, issn:
2331-8422, arXiv:1409.0473
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.
Gomes, G. S. S., et al. (2011). Comparison
of New Activation Functions in Neural Network for Forecasting Financial Time
Series, Neural Computing & Applications, 20: 417 – 439.
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.
Cambridge, Mass.: MIT Press
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
Ozkan, C., & Erbek, F. S.
(2003). The Comparison of Activation Functions for Multispectral Landsat TM
Image Classification, Photogrammetric Engineering & Remote Sensing, 69
(11): 1225 – 1234.
Paswan, R. P., et al. (2018).
Comparison of Abilities of Different Activation Functions of Artificial Neural
Networks to Predict Crop Area and Crop Production, International Journal of
Pure and Applied Bioscience, 6 (6): 212 – 220.
Qazi, A., Fayaz, H., Wadi, A., Raj,
R.G., Rahim, N.A., & Khan, W A (2015). The artificial neural network for
solar radiation prediction and designing solar systems: a systematic literature
review. Journal of Cleaner Production. 104, 1–12 (2015). https://doi.
org/10.1016/j.jclepro.2015.04.041
Raghupathi, V & Raghupathi, W
(2015). A neural network analysis of treatment quality and efficiency of
hospitals. J. Health Med. Inform. 2015, 6.
Ruder, S. (2017). An overview of
gradient descent optimization algorithms. Cornell University Library. ArXiv:
1609.04747.
Schmidhuber, J. (2014). Deep
learning in neural networks: An overview. Neural Networks, 61(2015), pp.
85-117.
Smartson P Nyoni., Thabani Nyoni
& Tatenda A Chihoho (2020) Prediction of new Covid-19 cases in Ghana using
artificial neural networks. IJARIIE Vol-6 Issue-6 2395-4396
Smartson. P. Nyoni., Thabani Nyoni
& Tatenda A. Chihoho (2020). Forecasting COVID-19 cases in Zimbabwe using
artificial neural networks, IJARIIE, 6, 6, 2395-4396
Smartson. P. Nyoni., Thabani Nyoni
& Tatenda A. Chihoho (2020). Forecasting COVID-19 cases in Ethiopia using
artificial neural networks, IJARIIE, 6, 6, 2395-4396
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
Weng SF., Reps J., Kai J.,
Garibaldi JM & Qureshi N (2017) Can machine-learning improve cardiovascular
risk prediction using routine clinical data? PLOS ONE 12(4): e0174944. https://doi.org/10.1371/journal.pone.0174944
Yan, H., Jiang, J., Zheng, J.,
Peng, C & Li, Q. (2006) A multilayer perceptron based medical decision
support system for heart disease diagnosis. Expert Syst. Appl. 2006, 30,
272–281.
Zhang G P (2003). “Time series
forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50:
159–175.
