Stochastic probical strategies in a delay virus infection model to combat COVID-19
•This work explores the internal viral dynamics with a specific type of nonlinear incidence rate.•The choice of incidence rate gives deeper insights and makes the study interesting.•We have identified the equilibrium points and the conditions of stability around equilibrium solutions.•The impact of...
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Veröffentlicht in: | Chaos, solitons and fractals solitons and fractals, 2021-11, Vol.152, p.111325-111325, Article 111325 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •This work explores the internal viral dynamics with a specific type of nonlinear incidence rate.•The choice of incidence rate gives deeper insights and makes the study interesting.•We have identified the equilibrium points and the conditions of stability around equilibrium solutions.•The impact of delay incorporation in a noisy environmental structure has been emphasized in this work.•The biological interpretations of the viral dynamics are presented to sketch a vivid picture.
In disease model systems, random noises and time delay factors play key role in interpreting disease dynamics to comprehend deeper insights into the course of dynamics. An endeavor to forecast intercellular behavioral dynamics of SARS-CoV-2 virus via Infection model with responsive host immune mechanisms forms the crux of this research study. Incorporation of time delay factor into infection transmission rates in noisy system epitomizes spectacular view on internal viral dynamics and stability properties are rigorously analyzed around equilibrium steady states to probe feasible strategies in mitigating rapid spread. Efforts to perceive inocular view on infection dynamics are not limited to theoretical frontiers but are substantiated with empirically simulated outcomes and visualized as graphical upshots. Discussions on numerical investigations emphasized shorter incubation periods and vaccination at pertinent time intervals to restrain massive spread and exhibit total immunity against SARS-CoV-2 infections. |
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ISSN: | 0960-0779 1873-2887 0960-0779 |
DOI: | 10.1016/j.chaos.2021.111325 |