The dynamics of entropy in the COVID-19 outbreaks

Autor: Arnaud Mignan, Marco Broccardo, Didier Sornette, Ziqi Wang
Rok vydání: 2020
Předmět:
0301 basic medicine
Physics - Physics and Society
Computer science
Bayesian probability
Bayesian analysis
Markov process
FOS: Physical sciences
Aerospace Engineering
Ocean Engineering
Physics and Society (physics.soc-ph)
Quantitative Biology - Quantitative Methods
Quantitative Biology::Other
03 medical and health sciences
symbols.namesake
0302 clinical medicine
COVID-19
Nonlinear Markov process
Stochastic process
Uncertainty quantification
Quantitative Biology::Populations and Evolution
030212 general & internal medicine
Statistical physics
Electrical and Electronic Engineering
Quantitative Biology - Populations and Evolution
Central element
Entropy rate
Quantitative Methods (q-bio.QM)
Original Paper
Applied Mathematics
Mechanical Engineering
Populations and Evolution (q-bio.PE)
030104 developmental biology
Control and Systems Engineering
FOS: Biological sciences
symbols
Probability distribution
Epidemic model
Zdroj: Nonlinear Dynamics
Nonlinear Dynamics, 101 (3)
ISSN: 0924-090X
1573-269X
Popis: 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.
Nonlinear Dynamics, 101 (3)
ISSN:0924-090X
ISSN:1573-269X
Databáze: OpenAIRE
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