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 New Zealand

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.

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. 449-454

doi.org/10.47001/IRJIET/2021.506078

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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.

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