The dynamics of entropy in the COVID-19 outbreaks

Wang, Ziqi1; Broccardo, Marco2,3; Mignan, Arnaud4,5; Sornette, Didier4,6

2020-09-09

10.1007/s11071-020-05871-5

NONLINEAR DYNAMICS   影响因子和分区

0924-090X

1573-269X

With the unfolding of the COVID-19 pandemic, mathematical modelling of epidemics has been perceived and used as a central element in understanding, predicting, and governing the pandemic event. However, soon it became clear that long-term predictions were extremely challenging to address. In addition, it is still unclear which metric shall be used for a global description of the evolution of the outbreaks. Yet a robust modelling of pandemic dynamics and a consistent choice of the transmission metric is crucial for an in-depth understanding of the macroscopic phenomenology and better-informed mitigation strategies. In this study, we propose a Markovian stochastic framework designed for describing the evolution of entropy during the COVID-19 pandemic together with the instantaneous reproductive ratio. Then, we introduce and use entropy-based metrics of global transmission to measure the impact and the temporal evolution of a pandemic event. In the formulation of the model, the temporal evolution of the outbreak is modelled by an equation governing the probability distribution that describes a nonlinear Markov process of a statistically averaged individual, leading to a clear physical interpretation. The time-dependent parameters are formulated by adaptive basis functions, leading to a parsimonious representation. In addition, we provide a full Bayesian inversion scheme for calibration together with a coherent strategy to address data unreliability. The time evolution of the entropy rate, the absolute change in the system entropy, and the instantaneous reproductive ratio are natural and transparent outputs of this framework. The framework has the appealing property of being applicable to any compartmental epidemic model. As an illustration, we apply the proposed approach to a simple modification of the susceptible-exposed-infected-removed model. Applying the model to the Hubei region, South Korean, Italian, Spanish, German, and French COVID-19 datasets, we discover significant difference in the absolute change of entropy but highly regular trends for both the entropy evolution and the instantaneous reproductive ratio.

COVID-19 Nonlinear Markov process Stochastic process Uncertainty quantification Bayesian analysis

SCI ; EI

英语

其他

Engineering ; Mechanics

Engineering, Mechanical ; Mechanics

WOS:000567741000001

SPRINGER

20203809185116

Epidemiology ; Nonlinear equations ; Transmissions ; Stochastic systems ; Probability distributions ; Entropy ; Markov processes ; Uncertainty analysis

Health Care:461.7 ; Mechanical Transmissions:602.2 ; Thermodynamics:641.1 ; Control Systems:731.1 ; Probability Theory:922.1 ; Systems Science:961

ENGINEERING

Web of Science

1.Guangzhou Univ, Earthquake Engn Res & Test Ctr, Guangzhou, Peoples R China
2.Univ Trento, Dept Civil Environm & Mech Engn, Trento, Italy
3.Univ Liverpool, Inst Risk & Uncertainties, Liverpool, Merseyside, England
4.Southern Univ Sci & Technol, Inst Risk Anal Predict & Management, Shenzhen, Peoples R China
5.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen, Peoples R China
6.Swiss Fed Inst Technol, Dept Management Technol & Econ, Chair Entrepreneurial Risks, Zurich, Switzerland

Wang, Ziqi,Broccardo, Marco,Mignan, Arnaud,et al. The dynamics of entropy in the COVID-19 outbreaks[J]. NONLINEAR DYNAMICS,2020,101(3):1847-1869.

Wang, Ziqi,Broccardo, Marco,Mignan, Arnaud,&Sornette, Didier.(2020).The dynamics of entropy in the COVID-19 outbreaks.NONLINEAR DYNAMICS,101(3),1847-1869.

Wang, Ziqi,et al."The dynamics of entropy in the COVID-19 outbreaks".NONLINEAR DYNAMICS 101.3(2020):1847-1869.