Dynamic Analysis of a Stochastic Delayed SEIRS Epidemic Model with Lévy Jumps and the Impact of Public Health Education

Zhou, Dake; Shi, Xiangyun; Zhou, Xueyong

doi:10.3390/axioms12060560

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Therefore, researchers began to use non Gaussian Lévy noise to depict these sudden environmental phenomena in nature [ 28 , 29 ]. Reference [ 30 ] presents a class of the stochastic time-delayed SEIRS epidemic model incorporating both public health education driven by Lévy noise and how to prevent and control diseases. Sabbar et al [ 31 ] established a stochastic COVID model under the influence of quadratic Lévy noise and quadratic jump-diffusion has no impact on the threshold value, but it remarkably influences the dynamics of the infection and may worsen the pandemic situation.…”

Section: Introductionmentioning

confidence: 99%

Stochastic dynamical analysis for the complex infectious disease model driven by multisource noises

Jian,

Bai,

2024

PLoS ONE

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This paper mainly studies the dynamical behavior of the infectious disease model affected by white noise and Lévy noise. First, a stochastic model of infectious disease with secondary vaccination affected by noises is established. Besides, the existence and uniqueness of the global positive solution for the stochastic model are proved based on stochastic differential equations and Lyapunov function, then the asymptotic behavior of the disease-free equilibrium point is studied. Moreover, the sufficient conditions for the extinction of the disease are obtained and the analysis showed that different noise intensity could affect the extinction of infectious disease on different degree. Finally, the theoretical results are verified by numerical simulation and some suggestions have been put forward on how to prevent the spread of diseases are presented.

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Section: Introductionmentioning

confidence: 99%

Stochastic dynamical analysis for the complex infectious disease model driven by multisource noises

Jian,

Bai,

2024

PLoS ONE

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Understanding underlying physical mechanism reveals early warning indicators and key elements for adaptive infections disease networks

Wang,

Zhang,

et al. 2024

PNAS Nexus

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The study of infectious diseases holds significant scientific and societal importance, yet current research on the mechanisms of disease emergence and prediction methods still face challenging issues. This research uses the landscape and flux theoretical framework to reveal the non-equilibrium dynamics of adaptive infectious diseases and uncover its underlying physical mechanism. This allows the quantification of dynamics, characterizing the system with two basins of attraction determined by gradient and rotational flux forces. Quantification of entropy production rates provides insights into the system deviating from equilibrium and associated dissipative costs. The study identifies early warning indicators for the critical transition, emphasizing the advantage of observing time irreversibility from time series over theoretical entropy production and flux. The presence of rotational flux leads to an irreversible pathway between disease states. Through global sensitivity analysis, we identified the key factors influencing infectious diseases. In summary, this research offers valuable insights into infectious disease dynamics and presents a practical approach for predicting the onset of critical transition, addressing existing research gaps.

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Predicting the trend of leptospirosis in China via a stochastic model with vector and environmental transmission

Shi,

Zhou,

Zhou

et al. 2024

era

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<abstract><p>A stochastic model of leptospirosis with vector and environmental transmission is established in this paper. By mathematical analysis of the model, the threshold for eliminating the disease is obtained. The partial rank correlation coefficient was used to analyze the parameters that have a greater impact on disease elimination, and a sensitivity analysis was conducted on the parameters through numerical simulation. Further, combined with the data of leptospirosis case reports in China from 2003 to 2021, two parameter estimation methods, Least Squares method (LSM) and Markov Chain Monte Carlo-Metropolis Hastings method (MCMC-MH), are applied to estimate the important parameters of the model and the future trend of leptospirosis in China are predicted.</p></abstract>

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Dynamic Analysis of a Stochastic Delayed SEIRS Epidemic Model with Lévy Jumps and the Impact of Public Health Education

Cited by 3 publications

References 32 publications

Stochastic dynamical analysis for the complex infectious disease model driven by multisource noises

Stochastic dynamical analysis for the complex infectious disease model driven by multisource noises

Understanding underlying physical mechanism reveals early warning indicators and key elements for adaptive infections disease networks

Predicting the trend of leptospirosis in China via a stochastic model with vector and environmental transmission

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