International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Forecasting Daily Covid-19 Cases in Burundi Using a Machine Learning Algorithm

Abstract

In this study, the ANN approach was applied to analyze daily new COVID-19 cases. The employed daily data covers the period 1 January 2020 to December 2020 and the out-of-sample period ranges over the period January 2021 to 31May 2021. The residuals and forecast evaluation criteria (Error, MSE and MAE) of the applied model indicate that the model is stable in forecasting daily new COVID-19 cases in Burundi. The results of the study indicate that daily COVID-19 cases are likely to be between 0-10 cases per day over the out of sample period. Therefore the government of Burundi must continue enforcing the implementation of WHO recommendations on the prevention and control of COVID-19.

Country : Zimbabwe

1 Dr. Smartson. P. NYONI2 Thabani NYONI3 Tatenda. A. CHIHOHO

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. SAGIT Innovation Centre, Harare, Zimbabwe
  3. Independent Researcher, Harare, Zimbabwe

IRJIET, Volume 5, Issue 3, March 2021 pp. 130-139

doi.org/10.47001/IRJIET/2021.503023

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References

  1. Abdul Samba Brima (2020). Burundi and COVID-19: Africa’s representation in the international media, African Journalism and Media in the Time of COVID-19, Essay series, pp 1-10.
  2. Arora, P., Kumar, H., & Panigrahi, B. K. (2020). Prediction and Analysis of COVID-19 Positive Cases Using Deep Learning Models: A Descriptive Case Study of India, Chaos, Solitons and Fractal, 139: 1 – 9.
  3. Bagcchi S (2020) COVID-19 and measles: double trouble for Burundi. The Lancet Microbe 1, e65.
  4. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  5. 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.
  6. Habonimana., L. Ouedraogo., E. Ndirahisha., N. Misago., R. Ciza., D. Niyomwungere., F. Niyongabo., J. B. Irakoze., J. D. Nkurunziza & S. Manirakiza (2020). Understanding the influence of the COVID-19 pandemic on hospital-based mortality in Burundi: a cross-sectional study comparing two time periods. Epidemiology and Infection 148, e280, 1–6. https://doi.org/10.1017/ S0950268820002770
  7. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992.
  8. Kishan Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks.
  9. Mo Ibrahim Foundation (MIF). COVID-19 in Africa: A call for coordinated governance, improved health structures and better data. Africa: April 8, 2020. Available from: https://mo.ibrahim
  10. 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
  11. OECD Development Matters. COVID-19 and beyond: How can Africa’s health systems cope? [cited 2020 Apr 14]. Available from: https://oecd-developmentmatters.org/2020/04/07/covid-19-and-beyond-howcan-africas-health-systems-cope/
  12. 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.
  13. 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.
  14. Smartson. P. Nyoni., Thabani Nyoni & Tatenda A. Chihoho (2020). Forecasting COVID-19 cases in Ethiopia using artificial neural networks, IJARIIE, 6, 6, 2395-4396
  15. 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
  16. WHO (2020) Coronavirus disease (COVID-19) pandemic updates for Burundi. Available at https://covid19.who.int/region/afro/country/bi (Accessed 19 July 2020 at 11:52 GMT+2
  17. WHO (2020) Coronavirus disease (COVID-19): situation report, 165.
  18. WHO (2020) Coronavirus disease 2019 (COVID-19): situation report, 88.
  19. WHO (2020). Coronavirus disease (COVID-19) pandemic updates. Available at https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (Accessed 19 July 2020 at 10:55 GMT + 2).
  20. Zhang G P (2003), “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.

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