Forecasting Covid-19 New Cases in New Zealand

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: 449-454

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/2021.506078

Abstract
In this study, the ANN approach was applied to analyze COVID-19 new cases in New Zealand. 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 New Zealand are generally likely to remain above 40 cases per day over the out-of-sample period. Amongst other suggested policy directions, there is need for the government of New Zealand 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 New Zealand” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 6, pp 449-454, June 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.506078

References
  1. Frank, A., & Grady, C. (2020, March 22). Phone booths, parades, and 10-minute test kits: How countries worldwide are fighting Covid-19. Vox. https://www.vox.com/science-and-health/2020/3/22/21189889/coronavirus-covid-19-pandemic-response-south-korea-phillipines-italy-nicaragu
  2. 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
  3. Nobre FF., Monteiro ABS., Telles PR., & Williamson GD (2001) Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology. Statistics in medicine 20: 3051–3069
  4. Nyoni, S. P., & Nyoni, T (2021). Forecasting ART coverage in Egypt using artificial neural networks. International Journal of Innovations in Engineering and Technology (IRJIET), 5 (3): 161-165.
  5. Paul Ho ., Thomas A. Lubik., & Christian Matthes (2020): Forecasting the COVID-19 epidemic: the case of New Zealand, New Zealand Economic Papers, DOI: 10.1080/00779954.2020.1842795
  6. Sibley, CG., Greaves, LG., Satherley, N, Wilson, M.S., Overall, N.C., Lee, C.H.J., Milojev, P., Bulbulia, J., Osborne, D., Milfont, T.L., Houkamau, C.A., Duck., I.M., Vickers-Jones, R., & Barlow, F.K. (in press). Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes towards government, and wellbeing. American Psychologist. 10.1037/amp0000662
  7. World Health Organization. (2020, January 21). Novel coronavirus (2019-nCoV) Situation Report – 1, 21 January 2020. https://www.who.int/docs/defaultsource/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf
  8. Zhang X., Zhang T., Young A A., & Li X (2014). Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data. PLoS ONE 9(2): e88075. doi:10.1371/journal.pone.0088075.