In this study, the ANN approach was applied to
analyze COVID-19 new cases in Cuba. 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 be around
814 over the out-of-sample period. Amongst other suggested policy directions,
there is need for the government of Cuba 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
CDC (2020).
The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, The
epidemiological characteristics of an outbreak of 2019 novel coronavirus
diseases (COVID-19) in China. China CDC Weekly 2, 113–122
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
Maradze, T.
C., Nyoni, S. P., & Nyoni, T (2021). Modeling and Forecasting Child
immunization against measles disease in Djibouti using artificial neural
networks (ANNs). International Journal of innovations in Engineering and
Technology (IRJIET), 5 (3):449-452
Maradze, T.
C., Nyoni, S. P., Nyoni, T (2021). Modeling and Forecasting COVID-19
mortalities in the United States of America using artificial neural networks
(ANN). International Journal of innovations in Engineering and Technology
(IRJIET), 5 (3):533-539
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
Nyoni, S.
P., & Nyoni, T (2021). Forecasting ART coverage in Kenya using the
Multilayer perceptron neural network. International Journal of Innovations in
Engineering and Technology (IRJIET), 5 (3): 161-165.
Nyoni, S.
P., & Nyoni, T (2021). Forecasting ART coverage in South Africa using the
Multilayer perceptron neural network. International Journal of Innovations in
Engineering and Technology (IRJIET), 5 (3): 207-211.
Nyoni, S.
P., & Nyoni, T (2021). Forecasting infant mortality rate in Gabon using
artificial neural networks, IRJIET, 5 (3): 592-596
Nyoni, S.
P., & Nyoni, T (2021). Predicting TB incidence in Zimbabwe: Artificial
neural networks reveal TB program success, IRJIET, 5(3): 380-384
Smartson.
P. Nyoni., Thabani Nyoni., Tatenda. A. Chihoho (2021). Forecasting daily new
Covid-19 cases in Botswana using artificial neural networks. International
Journal of innovations in Engineering and Technology (IRJIET), 5 (3):177-186.
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.
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. 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.