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DOI Prefix: 10.47001/IRJIET

Forecasting Covid-19 New Cases in Cabo Verde

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: 377-382

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/2021.506066

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Abstract

In this study, the ANN approach was applied to analyze COVID-19 new cases in Cabo Verde. 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 Carbo Verde are likely to increase over the out-of-sample period. Amongst other suggested policy directions, there is need for the authorities in Cabo Verde 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 Cabo Verde” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 6, pp 377-382, June 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.506066

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. 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
  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. 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
  5. 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
  6. Naizhuo Zhao., Katia Charlene., 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
  7. Nyoni & Nyoni T (2019). Forecasting TB notifications at Silobela District Hospital, Zimbabwe. IJARIIE 5(6)2395-4396.
  8. Nyoni & Nyoni T (2019). Forecasting TB notifications at Zengeza clinic, Zimbabwe.Online at https://mpra.ub.uni-muenchen.de/97331/ MPRA Paper No. 97331, posted 02 Dec 2019 10:13 UTC
  9. Nyoni S P & Nyoni T (2020). Modelling and forecasting infant deaths in Zimbabwe using ARIMA models. NOVATEUR PUBLICATIONS Journal- A Multidisciplinary Peer Reviewed Journal 6(7): 2581 – 4230.
  10. 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.
  11. 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.
  12. Nyoni, S. P., & Nyoni, T (2021). Predicting TB incidence in Zimbabwe: Artificial neural networks reveal TB program success, IRJIET, 5(3): 380-384
  13. 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.
  14. Zhang G P (2003). “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.

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