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 Covid-19 New Cases in Guyana

Abstract

In this study, the ANN approach was applied to analyze COVID-19 new cases in Guyana. 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 Guyana are likely to generally surge over the out-of-sample period. Amongst other suggested policy directions, there is need for the government of Guyana 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

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. SAGIT Innovation Center, Harare, Zimbabwe
  3. Independent Health Economist, Harare, Zimbabwe

IRJIET, Volume 5, Issue 6, June 2021 pp. 261-266

doi.org/10.47001/IRJIET/2021.506047

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References

  1. Althouse BM &Ng YY (2011). Cummings DAT, Prediction of dengue incidence using serach query surveillance. PLoS Neglected Tropical Diseases 2011; 5:e1258. https://doi.org/10.1371/journal.pntd.0001258 PMID: 21829744
  2. Dan W. Patterson (1995) Artificial Neural networks Theory and Applications. Singapore; New York: Prentice Hall.  
  3. 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.
  4. Gambhir S., Malik SK., & Kumar Y (2018). The diagnosis of dengue disease: An evaluation of three machine learning approaches. International Journal of Healthcare Information Systems and Informatics 2018; 13:1–19. https://doi.org/10.4018/ijhisi.2018040101 PMID: 3
  5. Guo P., Liu T., Zhang Q., Wang L., Xiao J & Zhang Q (2017). Developing a dengue forecast model using machine learning: A case study in China. PLoS Neglected Tropical Diseases 11:e0005973. https://doi.org/10.1371/journal.pntd.0005973 PMID: 29036169
  6. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-999
  7. Kishan Mehrotra., Chilukuri K., Mohan, & Sanjay Ranka (1997) Elements of artificial neural networks. Cambridge, Mass.: MIT Press 
  8. Laureano-Rosario AE., Duncvan AP., Mendez-Lazaro PA., Garcia-Rejon JE., Gomez-Carro S., & Farfan-Ale J (2018). Application of artificial neural networks for dengue fever outbreak predictions in the northwest coast of Yucatan, Mexico and San Juan, Puerto Rico. Tropical Medicine and Infectious Disease 2018;3:5
  9. S.K. Tamang., P.D. Singh., & B. Datta (2020). Forecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique, Global J. Environ. Sci. Manage. 6(SI): 53-64.
  10. Scavuzzo JM., Trucco F., Espinosa M., Tauro C B., Abril M., & Scavuzzo CM (2018). Modeling dengue vector population using remotely sensed data and machine learning. Acta Tropica 185:167–175. https://doi.org/10.1016/j.actatropica.2018.05.003 PMID: 29777650
  11. Smartson. P. Nyoni, Thabani Nyoni, Tatenda. A. Chihoho (2020) PREDICTION OF DAILY NEW COVID-19 CASES IN GHANA USING ARTIFICIAL NEURAL NETWORKS IJARIIE Vol-6 Issue-6             2395-4396
  12. 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
  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. 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
  15. Zhang G P, “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.

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