Intelligent Epidemiological Models for COVID-19
DOI:
https://doi.org/10.3384/ecp21185505Keywords:
intelligent methods, temporal analysis, dynamic modelling, digital twins, COVID-19Abstract
The coronavirus COVID-19 is affecting around the world with strong differences between countries and regions. Extensive datasets are available for visual inspection and downloading. The material has limitations for phenomenological modelling but data-based methodologies can be used. This research focuses on intelligent modelling on the basis of these datasets. The methodology has been tested in the analysis of daily new confirmed COVID-19 cases and deaths in six countries. The datasets are studied per million people to get comparable indicators. Nonlinear scaling brings the data of different countries to the same scale and linear interactions represent the varying operating conditions well. The same approach operates for both the confirmed cases and deaths and can be used for any country or group of people. The effects of the vaccinations were clearly shown at the end of the analyzed period. During the pandemic, the scaling functions expanded for the confirmed cases but remained practically unchanged for the confirmed deaths which is consistent with increasing testing. Limitations are seen if there are too many interacting things, e.g. several infection transmission chains which are in different stages. The feasibility analysis needs to be extended to the modelling with inputs. The presented approach is promising for this wider analysis.References
Our world in data. https://ourworldindata.org/, 2021. Accessed: 2021-07-10.
F. Arroyo-Marioli, F. Bullano, S. Kucinskas, and C. Rondón-Moreno. Tracking r of COVID-19: A new real-time estimation using the kalman filter. PLoS ONE, 16(1):1–16, 2021.
R. Babuška and H. Verbruggen. Neuro-fuzzy methods for nonlinear system identification. Annual Reviews in Control, 27(1):73–85, 2003.
G. E. P. Box and K. B. Wilson. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society. Series B, 13(1):1–45, 1951.
CSSE. COVID-19 data repository by the center for systems science and engineering (CSSE) at Johns Hopkins University. https://github.com/CSSEGISandData/COVID-19, 2021. Accessed: 2021-07-10.
E. Dong, H. Du, and L. Gardner. An interactive web-based dashboard to track covid-19 in real time. Lancet Inf Dis., 20(5):533–534, 2020. doi:10.1016/S1473-3099(20)30120-1.
R. W. Gerlach, B. R. Kowalski, and H. O. A. Wold. Partial least squares modelling with latent variables. Anal. Chim. Acta, 112(4):417–421, 1979.
Thomas Hale, Noam Angrist, Rafael AU Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, and Helen Tatlow. A global panel database of pandemic policies (oxford covid-19 government response tracker. Nature Human Behaviour, 5:529–538, 2021. doi:10.1038/s41562-021-01079-8.
T. Jefferson, E. A. Spencer, A. Plüddemann, N. Roberts, and C. Heneghan. Transmission dynamics of COVID-19: An open evidence review. https://www.cebm.net/evidence-synthesis/transmission-dynamics-of-covid-19/, 2021. Accessed: 2021-07-10.
I. T. Jolliffe. Principal Component Analysis. Springer, New York, 2 edition, 2002. 487 pp.
E. Juuso and S. Lahdelma. Intelligent scaling of features in fault diagnosis. In 7th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2010 - MFPT 2010, 22-24 June 2010, Stratford-upon-Avon, UK, volume 2, pages 1358–1372, 2010. URL www.scopus.com.
E. K. Juuso. Integration of intelligent systems in development of smart adaptive systems. International Journal of Approximate Reasoning, 35(3):307–337, 2004. doi:10.1016/j.ijar.2003.08.008.
E. K. Juuso. Tuning of large-scale linguistic equation (LE) models with genetic algorithms. In M. Kolehmainen, editor, Revised selected papers of the International Conference on Adaptive and Natural Computing Algorithms – ICANNGA 2009, Kuopio, Finland, Lecture Notes in Computer Science, volume LNCS 5495, pages 161–170. Springer-Verlag, Heidelberg, 2009. doi:10.1007/978-3-642-04921-7_17.
E. K. Juuso. Recursive tuning of intelligent controllers of solar collector fields in changing operating conditions. In S. Bittani, A. Cenedese, and S. Zampieri, editors, Proceedings of the 18th World Congress The International Federation of Automatic Control, Milano (Italy) August 28 - September 2, 2011, pages 12282–12288. IFAC, 2011. doi:10.3182/20110828-6-IT-1002.03621.
E. K. Juuso. Intelligent methods in modelling and simulation of complex systems. Simulation Notes Europe SNE, 24(1):1–10, 2014. doi:10.11128/sne.24.on.102221.
E. K. Juuso. Expertise and uncertainty processing with nonlinear scaling and fuzzy systems for automation. Open Engineering, 10(1):712–720, 2020. doi:10.1515/eng-2020-0080.
E. K. Juuso. Intelligent temporal analysis of coronavirus statistical data. Open Engineering, 2021. doi:10.1515/eng-2020-0285.
E. K. Juuso and K. Leiviskä. Adaptive expert systems for metallurgical processes. In S.-L. Jämsä-Jounela and A. J. Niemi, editors, Expert Systems in Mineral and Metal Processing, IFAC Workshop, Espoo, Finland, August 26-28, 1991, IFAC Workshop Series, 1992, Number 2, pages 119–124, Oxford, UK, 1992. Pergamon.
S. Lahdelma and E. Juuso. Signal processing and feature extraction by using real order derivatives and generalised norms. Part 1: Methodology. International Journal of Condition Monitoring, 1(2):46–53, 2011. doi:10.1784/204764211798303805.
L. Ljung. System Identification - Theory for the User. Prentice Hall, Upper Saddle River, N.J., 2nd edition, 1999.
L. Ljung. Perspectives on system identification. In M. J. Chung and P. Misra, editors, Plenary papers, milestone reports & selected survey papers, 17th IFAC World Congress, Seoul, Korea, July 6-11, 2008, pages 47–59. IFAC, 2008. http://www.ifac-papersonline.net/.
Hannah Ritchie, Esteban Ortiz-Ospina, Diana Beltekian, Edouard Mathieu, Joe Hasell, Bobbie Macdonald, Charlie Giattino, Cameron Appel, Lucas Rodés-Guirao, and Max Roser. Coronavirus pandemic (COVID-19). Our World in Data, 2020. https://ourworldindata.org/coronavirus.
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