The dynamics of entropy in the COVID-19 outbreaks

被引:0
作者
Ziqi Wang
Marco Broccardo
Arnaud Mignan
Didier Sornette
机构
[1] Guangzhou University,Earthquake Engineering Research and Test Center
[2] University of Trento,Department of Civil, Environmental and Mechanical Engineering
[3] University of Liverpool,Institute for Risk and Uncertainties
[4] Southern University of Science and Technology,Institute of Risk Analysis, Prediction and Management
[5] Southern University of Science and Technology,Department of Earth and Space Sciences
[6] ETH Zürich,Chair of Entrepreneurial Risks, Department of Management, Technology, and Economics
来源
Nonlinear Dynamics | 2020年 / 101卷
关键词
COVID-19; Nonlinear Markov process; Stochastic process; Uncertainty quantification; Bayesian analysis;
D O I
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中图分类号
学科分类号
摘要
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.
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页码:1847 / 1869
页数:22
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