How big of an impact do asymptomatic people have on the dynamics of an epidemic?

Hossain, Md. Biplob; Masud, Mehedi; Sikder, Aruk Kumar; Islam, Hamidul

doi:10.1016/j.csfx.2023.100093

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…In contrast, the multi-scroll chaotic attractor with complex topological structure has greater application potential, which is suitable for image encryption, speech encryption and other secure communication fields [21,22]. Using complex arithmetic, multiple folds of hidden Chua's attractors can be generated [23]. For special case of no-equilibrium system, the multi-scroll and grid scroll hidden attractors could be manufactured via entending the number of perpetual points [24].…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al purposed a new piecewise polynomial magnetron memristor and introduced it into HR neural network to produce a controllable amount of vortex attractors [22]. Lin et al acquired multi-coil attractors with diverse volumes by adding two external stimulation, namely electromagnetic radiation and multilevel logic pulses to HNN [23]. However, the abovesaid neural network models for generating multiple scrolls are usually implemented using memristors with multi-segment nonlinear functions.…”

Section: Introductionmentioning

confidence: 99%

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A novel memristor Hopfield neural network with homogeneous coexisting multi-scroll attractors

Huang,

Chen,

Yang

et al. 2024

Phys. Scr.

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Compared to conventional single-scroll or double-scroll attractors, multi-scroll chaotic attractors possess wide potential for application due to their adjustability and complex topology. However, neural network models for generating multiple scrolls are often implemented using memristors with piecewise nonlinear functions. To further explore multi-scroll attractors with different working mechanisms,a unique memristor containing a group of hyperbolic tangent functions is designed and then applied in a three-dimensional Hopfield neural network (HNN). The proposed memristive Hopfield neural network (MHNN) has multi-scroll chaotic attractors, where the number and parity of the scrolls be changed by adjusting the control parameters of the memristor. The complex dynamical behaviors of MHNN are studied by utilizing diverse numerical modeling approaches like bifurcation diagrams, Lyapunov exponents and phase plot. In addition, the proposed MHNN also has a complicated offset boosting coexisting behavior. By selecting suitable parameters, multiple coexisting chaotic attractors could be obtained. Homogeneous coexisting multi-scroll attractors can be shifted in multiple directions including unidirectional, planar and spatial ones. Moreover, theoretically speaking, there could be an infinite number of coexisting attractors. Finally, experimental results are validated through numerical simulations and circuit experiments to confirm the feasibility of the proposed MHNN model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel memristor Hopfield neural network with homogeneous coexisting multi-scroll attractors

Huang,

Chen,

Yang

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

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Stochastic behavior of within-host progression in primary dengue infection

Islam,

Masud,

Kim

2024

J. Appl. Math. Comput.

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Dengue is a mosquito-borne viral infection that triggers a series of intracellular events in the host immune system, which may result in an invasion of the virus into the host and cause illness with a spectrum of severity. Depending on the degree of the infection, mild to severe clinical symptoms appear when the T-cell and B-cell-initiated immune responses fail to eradicate the virus particles and subsequently become compromised. Here, we propose a mathematically tractable simple model that exhibits important biological features of dengue infection. Dynamical analysis of our model explores the factors influencing viral persistence in the body over an extended period. To investigate plausible variability in viral dynamics in different hosts, we perform stochastic simulations of our model using Gillespie’s algorithm. Our simulation results recapitulate the distribution of the intrinsic incubation period, daily viral load, and the day of peak viremia. In addition, we observe that the invasion probability of the virus into the host is correlated with the initial virus population injected by the mosquito. However, considering the biting behavior of Aedes mosquitoes, a lower initial virus injection could end up increasing the epidemic potential of the virus.

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How big of an impact do asymptomatic people have on the dynamics of an epidemic?

Cited by 2 publications

References 54 publications

A novel memristor Hopfield neural network with homogeneous coexisting multi-scroll attractors

A novel memristor Hopfield neural network with homogeneous coexisting multi-scroll attractors

Stochastic behavior of within-host progression in primary dengue infection

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