Forecasting Models of the Coronavirus (COVID-19) Cumulative Confirmed Cases Using a Hybrid Genetic Programming Method



Abstract Views
437

Downloads
353

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Aug 31, 2020
Published Dec 15, 2020
10.24018/ejeng.2020.5.12.2129

Abstract viewed = 437 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

The coronavirus disease 2019 (COVID-19) diffusion process, starting in China, caused more than 4600 lives until June 2020 and became a major threat to global public health systems. In Greece, the phenomenon started on February 2020 and it is still being developed. This paper presents the implementation of a hybrid Genetic Programming (hGP) method in finding fitting models of the Coronavirus (COVID 19) for the cumulative confirmed cases in China for the first saturation level until May 2020 and it proposes forecasting models for Greece before summer tourist season. The specific hGP method encapsulates the use of some well-known diffusion models for forecasting purposes, epidemiological models and produces time dependent models with high performance statistical indices. A retrospective study confirmed the excellent forecasting performance of the method until 3 June 2020.

Keywords: Genetic Programming; Evolutionary algorithms; diffusion models; corona virus; forecasting

Downloads

Download data is not yet available.

References

  1. Hethcote, H. (2000). The Mathematics of Infectious Diseases. SIAM Review, 42(4), 599-653. Retrieved April 20, 2020, from www.jstor.org/stable/2653135.
     Google Scholar
  2. Kermack, W. O. and McKendrick, A. G. (1927). Contributions to the mathematical theory of epidemics, part i. Proceedings of the Royal Society of Edinburgh. Section A. Mathematics. 115 700–721.
     Google Scholar
  3. Kermack, W. O. and McKendrick, A. G. (1932). Contributions to the mathematical theory of epidemics, ii - the problem of endemicity. Proceedings of the Royal Society of Edinburgh. Section A. Mathematics. 138 55–83.
     Google Scholar
  4. Kermack, W. O. and McKendrick, A. G. (1933). Contributions to the mathematical theory of epidemics, iii - further studies of the problem of endemicity. Proceedings of the Royal Society of Edinburgh. Section A. Mathematics. 141 94–122.
     Google Scholar
  5. Hethcote H.W. (1989) Three Basic Epidemiological Models. In: Levin S.A., Hallam T.G., Gross L.J. (eds) Applied Mathematical Ecology. Biomathematics, vol 18. Springer, Berlin, Heidelberg.
     Google Scholar
  6. Shabbir G., Khan H., Sadiq M., “A note on Exact solution of SIR and SIS epidemic models.”, e-prints arXiv:1012.5035, 2010.
     Google Scholar
  7. Weisstein, Eric W. "SIR Model." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/SIRModel.html.
     Google Scholar
  8. Salpasaranis Konstantinos and Stylianakis Vasilios, “A New Empirical Model for Short-Term Forecasting of the Broadband Penetration: A Short Research in Greece,” Modelling and Simulation in Engineering, vol. 2011, Article ID 798960, 10 pages, 2011. doi:10.1155/2011/798960.
     Google Scholar
  9. Konstantinos Salpasaranis and Vasilios Stylianakis, “A Hybrid Genetic Programming Method in Optimization and Forecasting: A Case Study of the Broadband Penetration in OECD Countries,” Advances in Operations Research, vol. 2012, Article ID 904797, 32 pages, 2012. doi:10.1155/2012/904797.
     Google Scholar
  10. Konstantinos Salpasaranis, Vasilios Stylianakis, and Stavros Kotsopoulos, “Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries,” Advances in Operations Research, vol. 2014, Article ID 568478, 20 pages, 2014. doi:10.1155/2014/568478.
     Google Scholar
  11. Konstantinos Salpasaranis, Vasilios Stylianakis, “Forecasting the OECD Fixed Broadband Penetration with Genetic Programming method, diffusion models and macro-economic indicators”, Image Processing & Communications, 2017 http://ipc.utp.edu.pl/index.php/ipc/article/view/65.
     Google Scholar
  12. Bass F. M., “A new product growth for model consumer durables,” Management Science, vol. 15, no. 5, pp. 215–227, 1969.
     Google Scholar
  13. P. Meyer, “Bi-logistic growth,” Technological Forecasting and Social Change, vol. 47, no. 1, pp. 89–102, 1994.
     Google Scholar
  14. P. S. Meyer and J. H. Ausubel, “Carrying capacity: a model with logistically varying limits,” Technological Forecasting and Social Change, vol. 61, no. 3, pp. 209–214, 1999.
     Google Scholar
  15. M. N. Sharif and K. Ramanathan, “Binomial innovation diffusion models with dynamic potential adopter population,” Technological Forecasting and Social Change, vol. 20, no. 1, pp. 63–87, 1981.
     Google Scholar
  16. T. Kamalakis, I. Neokosmidis, D. Varoutas, and T. Sphicopoulos, “Demand and price evolution forecasting as tools for facilitating the roadmapping process of the photonic component industry,” in Proceedings of the World Academy Of Science, Engineering And Technology, vol. 18, 2006.
     Google Scholar
  17. Holland, J. H., “Adaptation in Natural and Artificial Systems”, University of Michigan Press, 1975.
     Google Scholar
  18. Koza J.R., “Genetic Programming: On the programming of Computers by Means of Natural Selection”, The MIT Press (1992).
     Google Scholar
  19. Koza J.R., “Genetic programming for economic modeling”, Statistics and Computing 4(2), 187–197 (1994).
     Google Scholar
  20. Weisstein, Eric W. "Exponential Integral." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/ExponentialIntegral.html.
     Google Scholar
  21. Weisstein, Eric W. "Gompertz Constant." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/GompertzConstant.html.
     Google Scholar
  22. World Health Organization (WHO), https://covid19.who.int/region/wpro/country/cn, April 2020.
     Google Scholar
  23. World Health Organization (WHO), https://covid19.who.int/region/wpro/country/cn, June 2020.
     Google Scholar