Impact of information intervention on stochastic dengue epidemic model

Liu, Yijie; Din, Anwarud; Zenab,

doi:10.1016/j.aej.2021.03.068

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…For example, Raja et al demonstrated nonsingular heat conduction equation under SDE [20]. In recent years, models of epidemics have been examined using SDEs, including those related to Hepatitis B disease [21,22], influenza disease [23], dengue transmission [24], co-infection model [25] and COVID-19 models [26][27][28].…”

Section: Formulation Of the Modelmentioning

confidence: 99%

Insights into COVID-19 stochastic modelling with effects of various transmission rates: simulations with real statistical data from UK, Australia, Spain, and India

Xu,

Pang,

Liu

et al. 2024

Phys. Scr.

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In the literature \cite{saeed}, the COVID-19 model has been constructed using deterministic approach. The present manuscript examines a stochastic model designed to capture the interplay between COVID-19 and varying infection rates on disease dynamics. We present the necessary criteria for a global solution to the considered model to exist and be unique. To illustrate several outcomes pertaining to the ergodic properties of the given system, the we utilize nonlinear analysis. Furthermore, the model undergoes simulation and is compared with deterministic dynamics. To verify the efficacy of the considered model and demonstrate its utility, we compare the dynamics of the infected population to real statistical data from multiple countries, such as the United Kingdom, Australia, Spain, and India. The proposed model has proven to be a reliable and effective tool for understanding the intricate nature of COVID-19 dynamics. Moreover, we provide a visually striking depiction of the impact of different infection rates on the propagation of the model under investigation. This visualization provides valuable insight into the multifaceted nature of the pandemic and significantly contributes to the comprehension of COVID-19 dynamics.

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Section: Formulation Of the Modelmentioning

confidence: 99%

Insights into COVID-19 stochastic modelling with effects of various transmission rates: simulations with real statistical data from UK, Australia, Spain, and India

Xu,

Pang,

Liu

et al. 2024

Phys. Scr.

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“…Peijiang et. al [11] carried out investigation on stochastic disturbances involving both human and mosquito populations according to the characteristics of the diseases.They used suitable Lyapunov function, the uniqueness and existence of positive solution to the stochastic differential equation model and also investigated exponential stability of the problem. Asamoah et al [12] analysed an optimal control model of dengue infection with partial immune and asymptomatic individuals.…”

Section: Introductionmentioning

confidence: 99%

Modelling the super-infection of two strains of dengue virus

2023

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Dengue is one of the vector borne diseases that threatened human race. It is imperative to understand the transmission dynamics of dengue, so that proficient and useful control can be developed. In this paper, we formulated dynamic transmission of two strains super-infection dengue. We used next generation matrix to obtain the basic reproduction numbers $${\mathcal {R}}_1$$ R 1 , $${\mathcal {R}}_2$$ R 2 , $${\mathcal {R}}_{12}$$ R 12 . The obtained basic reproduction numbers are then used to test for stabilities whenever $${\mathcal {R}}_1<1$$ R 1 < 1 , $${\mathcal {R}}_2<1$$ R 2 < 1 , $${\mathcal {R}}_{12}<1$$ R 12 < 1 , disease free-equilibrium is globally asymptotically stable or otherwise unstable. We also carried out numerical simulation with the aid of python software. Our results reveal that decreasing the value of tansmission probability $$\alpha ,$$ α , slows down the spread of the disease.

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Analysis of a Stochastic Within-Host Model of Dengue Infection with Immune Response and Ornstein–Uhlenbeck Process

Liu,

Jiang

2024

J Nonlinear Sci

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Impact of information intervention on stochastic dengue epidemic model

Cited by 10 publications

References 31 publications

Insights into COVID-19 stochastic modelling with effects of various transmission rates: simulations with real statistical data from UK, Australia, Spain, and India

Insights into COVID-19 stochastic modelling with effects of various transmission rates: simulations with real statistical data from UK, Australia, Spain, and India

Modelling the super-infection of two strains of dengue virus

Analysis of a Stochastic Within-Host Model of Dengue Infection with Immune Response and Ornstein–Uhlenbeck Process

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