Forecasting Covid-19 New Cases in Grenada

Abstract

In this study, the ANN approach was applied to analyze COVID-19 new cases in Grenada. 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 Grenada are likely to be almost zero over the out-of-sample period. Amongst other suggested policy directions, there is need for the government of Grenada 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. 218-223

doi.org/10.47001/IRJIET/2021.506040

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. 29,985-992
  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. 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
  10. Qingchun Guo & Zhenfang He (2020). Environmental Science and Pollution Research (2021) 28:11672–11682.
  11. 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
  12. 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 
  13. 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
  14. Weng S F., Reps J., Kai J., Garibaldi J M., & 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 (2003), “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.