Non-integer (or fractional) power model to represent the complexity of a viral spreading: Application to the COVID-19

被引:3
作者
Oustaloup, Alain [1 ]
Levron, Francois [2 ]
Victor, Stephane [1 ]
Dugard, Luc [3 ]
机构
[1] Univ Bordeaux, Bordeaux INP ENSEIRB MATMECA, CNRS, IMS UMR 5218, 351 Cours Liberat, F-33405 Talence, France
[2] Univ Bordeaux, Bordeaux INP ENSEIRB MATMECA, CNRS, IMB UMR 5251, 351 Cours Liberat, F-33405 Talence, France
[3] Univ Grenoble Alpes, GIPSA Lab, Grenoble INP, CNRS, F-38000 Grenoble, France
关键词
COVID-19; Viral spreading; Modeling; Prediction; Time series; Fractional (or non-integer) power model (FPM); A FPM model as a convexity or concavity model; Power-lawInternal dynamics dispersion; Non-integer (or fractional) differentiation or integration; Self-filtering internal structure; DYNAMICS; IDENTIFICATION;
D O I
10.1016/j.arcontrol.2021.09.003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article proposes a very simple deterministic mathematical model, which, by using a power-law, is a non-integer power model (or fractional power model (FPM)). Such a model, in non-integer power of time, namely t(m) up to constants, enables representing the totality of the contaminated individuals at each day, with a good precision, thus expressing the interest of this tool for time series. Despite being enriched with knowledge through an internal structure based on a geometric sequence "with variable ratio", the model (in its non-integer representation) has only three parameters, among which the non-integer power, m, that determines on its own, according to its value, an aggravation or an improvement of the viral spreading. Its simplicity comes from the power-law, t(m), which simply expresses the singular dynamics of the operator of non-integer differentiation or integration, of high parametric compactness, that governs diffusion phenomena and, as shown in this article, the spreading phenomena by contamination. The representativity of the proposed model is indeed validated with the official data of French Ministry of Health on the COVID-19 spreading, notably the time series of the contaminations and the hospitalizations. Used in prediction, the model well enables justifying the choice of a lockdown, without which the spreading would have highly worsened. Its predictivity is validated by verified predictions in lockdown and vaccination phases, and even for the vaccination itself; its simplicity enables a very simple implementation of the prediction technique. The comparison of this model in t(m) with two known models having the same number of parameters, well shows that its representativity of the real data is better or more general. Finally, in a more fundamental context and particularly in terms of complexity and simplicity, a self-filtering action enables showing the compatibility between the internal complexity that the internal structure and its stochastic behavior present, and the global simplicity that the model in t(m) offers in a deterministic manner: it is true that the non-integer power of a power-law, that an internal dynamics dispersion can justify, is well a marker of complexity, and this, beyond viral spreading phenomena.
引用
收藏
页码:523 / 542
页数:20
相关论文
共 64 条
  • [1] Power-law solvation dynamics in DNA over six decades in time
    Andreatta, D
    Lustres, JLP
    Kovalenko, SA
    Ernsting, NP
    Murphy, CJ
    Coleman, RS
    Berg, MA
    [J]. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2005, 127 (20) : 7270 - 7271
  • [2] [Anonymous], 2003, MATH GAZ, DOI [DOI 10.1017/S0025557200173802, 10.1017/S0025557200173802]
  • [3] A Caputo power law model predicting the spread of the COVID-19 outbreak in Pakistan
    Arfan, Muhammad
    Shah, Kamal
    Abdeljawad, Thabet
    Mlaiki, Nabil
    Ullah, Aman
    [J]. ALEXANDRIA ENGINEERING JOURNAL, 2021, 60 (01) : 447 - 456
  • [5] Heat flux estimation through inverted non-integer identification models.
    Battaglia, JL
    Le Lay, L
    Batsale, JC
    Oustaloup, A
    Cois, O
    [J]. INTERNATIONAL JOURNAL OF THERMAL SCIENCES, 2000, 39 (03) : 374 - 389
  • [6] Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario
    Carli, Raffaele
    Cavone, Graziana
    Epicoco, Nicola
    Scarabaggio, Paolo
    Dotoli, Mariagrazia
    [J]. ANNUAL REVIEWS IN CONTROL, 2020, 50 : 373 - 393
  • [7] The COVID-19 pandemic: model-based evaluation of non-pharmaceutical interventions and prognoses
    De Visscher, Alex
    [J]. NONLINEAR DYNAMICS, 2020, 101 (03) : 1871 - 1887
  • [8] Particle modeling of the spreading of coronavirus disease (COVID-19)
    De-Leon, Hilla
    Pederiva, Francesco
    [J]. PHYSICS OF FLUIDS, 2020, 32 (08)
  • [9] Solvable delay model for epidemic spreading: the case of Covid-19 in Italy
    Dell'Anna, Luca
    [J]. SCIENTIFIC REPORTS, 2020, 10 (01)
  • [10] SI epidemic model applied to COVID-19 data in mainland China
    Demongeot, J.
    Griette, Q.
    Magal, P.
    [J]. ROYAL SOCIETY OPEN SCIENCE, 2020, 7 (12):