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An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data

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  • Papageorgiou, Vasileios E.
  • Tsaklidis, George

Abstract

The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date, concerning national health systems on a daily basis, since December 2019 when it appeared in Wuhan City. Nevertheless, most of the proposed mathematical methodologies aiming to describe the dynamics of an epidemic, rely on deterministic models that are not able to reflect the true nature of its spread. In this paper, we propose a SEIHCRDV model – an extension/improvement of the classic SIR compartmental model – which also takes into consideration the populations of exposed, hospitalized, admitted in intensive care units (ICU), deceased and vaccinated cases, in combination with an unscented Kalman filter (UKF), providing a dynamic estimation of the time dependent system's parameters. The stochastic approach is considered necessary, as both observations and system equations are characterized by uncertainties. Apparently, this new consideration is useful for examining various pandemics more effectively. The reliability of the model is examined on the daily recordings of COVID-19 in France, over a long period of 265 days. Two major waves of infection are observed, starting in January 2021, which signified the start of vaccinations in Europe providing quite encouraging predictive performance, based on the produced NRMSE values. Special emphasis is placed on proving the non-negativity of SEIHCRDV model, achieving a representative basic reproductive number R0 and demonstrating the existence and stability of disease equilibria according to the formula produced to estimateR0. The model outperforms in predictive ability not only deterministic approaches but also state-of-the-art stochastic models that employ Kalman filters. Furthermore, the relevant analysis supports the importance of vaccination, as even a small increase in the dialy vaccination rate could lead to a notable reduction in mortality and hospitalizations.

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  • Papageorgiou, Vasileios E. & Tsaklidis, George, 2023. "An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:chsofr:v:166:y:2023:i:c:s0960077922010931
    DOI: 10.1016/j.chaos.2022.112914
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    References listed on IDEAS

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    1. Luis Rosero-Bixby & Tim Miller, 2022. "The mathematics of the reproduction number R for Covid-19: A primer for demographers," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 20(1), pages 143-166.
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    4. Kostas Loumponias & George Tsaklidis, 2022. "Kalman filtering with censored measurements," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(2), pages 317-335, January.
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    7. Song, Jialu & Xie, Hujin & Gao, Bingbing & Zhong, Yongmin & Gu, Chengfan & Choi, Kup-Sze, 2021. "Maximum likelihood-based extended Kalman filter for COVID-19 prediction," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
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    Cited by:

    1. Marcin Mazur & Jerzy Bański & Wioletta Kamińska, 2024. "The Geographical Conditioning of Regional Differentiation Characterising the COVID-19 Pandemic in European Countries," IJERPH, MDPI, vol. 21(10), pages 1-20, October.
    2. Vasileios E. Papageorgiou & Georgios Vasiliadis & George Tsaklidis, 2023. "Analyzing the Asymptotic Behavior of an Extended SEIR Model with Vaccination for COVID-19," Mathematics, MDPI, vol. 12(1), pages 1-12, December.

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