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

Modelling and Forecasting Covid-19 Mortalities in the United Kingdom Using Artificial Neural Networks (ANN)

Abstract

In this research article, the ANN approach was applied to analyze COVID-19 deaths in the United Kingdom (UK). The employed data covers the period January – December 2020 and the out-of-sample period ranges over the period January – May 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 thatCOVID-19 deaths will be generally between 200 and 1000 deaths per day in the UK over the out-of-sample period. The UK government ought to be cautious, particularly in the relaxation of any controls. This will ensure that the most vulnerable members of society are protected, especially those with chronic conditions.

Country : Zimbabwe

1 Mr. Takudzwa. C. Maradze2 Dr. Smartson. P. NYONI3 Mr. Thabani NYONI

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

IRJIET, Volume 5, Issue 3, March 2021 pp. 551-557

doi.org/10.47001/IRJIET/2021.503093

Full Paper
Download

References

  1. Bhopal, Sunil, Jay Bagaria, and Raj Bhopal. "Children's mortality from COVID-19 compared with all-deaths and other relevant causes of death: epidemiological information for decision-making by parents, teachers, clinicians and policymakers." Public health 185 (2020): 19.
  2. Brazeau, N., Robert Verity, Sara Jenks, Han Fu, Charles Whittaker, Peter Winskill, IlariaDorigatti et al. "Report 34: COVID-19 infection fatality ratio: estimates from seroprevalence." (2020).
  3. Cereda, Diletta, Marcello Tirani, Francesca Rovida, Vittorio Demicheli, Marco Ajelli, PieroPoletti, FrédéricTrentini et al. "The early phase of the COVID-19 outbreak in Lombardy, Italy." arXiv preprint arXiv:2003.09320 (2020).
  4. Holman, Naomi, Peter Knighton, ParthaKar, Jackie O'Keefe, Matt Curley, Andy Weaver, Emma Barron et al. "Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: a population-based cohort study." The lancet Diabetes & endocrinology 8, no. 10 (2020): 823-833.
  5. Kucharski, Adam J., Timothy W. Russell, Charlie Diamond, Yang Liu, John Edmunds, Sebastian Funk, Rosalind M. Eggo et al. "Early dynamics of transmission and control of COVID-19: a mathematical modelling study." The lancet infectious diseases 20, no. 5 (2020): 553-558.
  6. Tamang, S. K., P. D. Singh, and B. Datta. "Forecasting of covid-19 cases based on prediction using artificial neural network curve fitting technique." Global Journal of Environmental Science and Management 6, no. Special Issue (Covid-19) (2020): 53-64.
  7. West, Anne, Thomas Czypionka, Monika Steffen, Stefanie Ettelt, Simone Ghislandi, and CéuMateus. Reporting COVID-19 deaths in Austria, France, Germany, Italy, Portugal and the UK. Department of Social Policy, London School of Economics and Political Science, 2020.
  8. World Health Organization. "Novel Coronavirus (‎ 2019-nCoV)‎: situation report, 3." (2020). https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov. pdf?sfvrsn=20a99c10_4. Accessed 12 March  2021
  9. Zhu, Na, Dingyu Zhang, Wenling Wang, Xingwang Li, Bo Yang, Jingdong Song, Xiang Zhao et al. "A novel coronavirus from patients with pneumonia in China, 2019." New England journal of medicine (2020).

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies