In this study, the ANN approach was applied to
analyze COVID-19 new cases in Georgia. 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 COVID-19 cases are likely to vanish
around early April 2021 over the out-of-sample period. Amongst other suggested
policy directions, there is need for the government of Georgia 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
Al-Qaness., M.A.,
Ewees, A.A., Fan, H., Abd El Aziz, M (2020). Optimization method for forecasting
confirmed cases of COVID-19 in China. J. Clin. Med. 2020, 9, 674.
Eriksson, T.A., Bülow,
H., & Leven, A (2017). Applying neural networks in optical communication
systems: Possible pitfalls. IEEE Photonics Technol. Lett, 29, 2091–2094.
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
Hamadneh, N.N., Khan,
W.A., Ashraf, W., Atawneh, S.H., Khan, I., & Hamadneh, B.N (2021).
Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia. Comput.
Mater. Contin, 66, 2787–2796
Hamadneh, N.N.; Tahir,
M.; Khan, W.A. Using Artificial Neural Network with Prey Predator Algorithm for
Prediction of the COVID-19: The Case of Brazil and Mexico. Mathematics 2021, 9,
180. https://doi.org/math9020180
Kaushik AC & Sahi.
S (2018). Artificial neural network-based model for orphan
GPCRs.Neural.Comput.Appl. 29,985-992
Kong, W., &
Agarwal, P.P (2020). Chest imaging appearance of COVID-19 infection. Radiol.
Cardiothoracic. Imaging 2020, 2, e200028.
Meng, F., Uversky,
V.N., & Kurgan, L (2017). Comprehensive review of methods for prediction of
intrinsic disorder and its molecular functions. Cell. Mol. Life Sci. 2017, 74,
3069–3090.
Tang. B et al
(2020). An updated estimation of the
risk of transmission of the novel coronavirus (2019-nCoV). Infectious Disease
Modelling 5, 248–255.
Tang. B et al (2020).
Estimation of the transmission risk of the 2019-nCoV and its implication for
public health interventions. J. Clinical Med. 9, 462.
Tartaglione, E.,
Barbano, C.A., Berzovini, C., Calandri, M., & Grangetto, M (2020).
Unveiling COVID-19 from Chest X-ray with deep learning: A hurdles race with
small data. Int. J. Environ. Res. Public Health 2020, 17, 6933.
Wang D et al
(2020). Clinical characteristics of 138
hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan,
China. JAMA 323, 1061–1069.
Wang H et al (2020).
Phase-adjusted estimation of the number of coronavirus disease 2019 cases in
Wuhan, China. Cell Discovery 6, 76.
Wang, L., Wang, Z., Qu,
H., & Liu, S (2018). Optimal forecast combination based on neural networks
for time series forecasting. Appl. Soft Comput. 2018, 66, 1–17.
Wang. W et al (2020).
Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in
Wuhan, China. J. Med. Virology 92, 441–447
Zhang G P (2003). “Time
series forecasting using a hybrid ARIMA and neural network model”,
Neurocomputing 50: 159–175
Zu, Z.Y., Jiang, M.D.,
Xu, P.P., Chen, W., Ni, Q.Q., Lu, G.M., & Zhang, L.J (2020). Coronavirus
disease 2019 (COVID-19): A perspective from China. Radiology 2020, 200490.