What does this research mean for the field?

Incorporating memory effects via Caputo fractional derivatives in computer virus models demonstrates that stronger memory effects delay virus spread, while stochastic perturbations significantly influence infection outcomes. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and evaluate a fractional-order mathematical model incorporating historical memory traces and stochastic noise to investigate computer virus propagation and optimal control strategies.

September 14, 2026International Journal of Modern Physics C

Incorporating Memory Traces in Computer Virus Models Using Caputo Fractional Calculus

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Authors

MAMuflih Alhazmi DADalal hadri Albaqami NANajat Almutairi

Discussion

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Overview

Mathematical modeling demonstrates that fractional memory effects and stochastic perturbations delay computer virus propagation in network populations, suggesting optimized cybersecurity control...

Key Points

To develop and evaluate a fractional-order mathematical model incorporating historical memory traces and stochastic noise to investigate computer virus propagation and optimal control strategies.
Formulated a four-compartment epidemiological model (susceptible, latently infected, breaking-out, and antivirus-capable) using Caputo fractional derivatives.
Conducted qualitative analyses for existence, uniqueness, boundedness, basic reproduction number ($R_0$), and forward transcritical bifurcation.
Performed stochastic simulations via the Euler–Maruyama method and applied Pontryagin’s Maximum Principle to evaluate optimal prevention, treatment, and screening strategies.
The basic reproduction number ($R_0$) acts as a sharp threshold parameter governing the global stability of virus-free and endemic steady states.
Numerical simulations showed that stronger memory effects (lower fractional orders) substantially delay the rate of virus transmission across the network.
Sensitivity analyses identified the virus transmission rate and latency transition rate as the most critical determinants of outbreak severity.

Cite This Study

Alhazmi et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b2f70926e14a848b196b https://doi.org/10.1142/s012918312642009x

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September 14, 2026International Journal of Modern Physics C

Incorporating Memory Traces in Computer Virus Models Using Caputo Fractional Calculus

View Full Paper

Ask AI

Bookmark

Authors

MAMuflih Alhazmi DADalal hadri Albaqami NANajat Almutairi

Discussion

Member takes

Overview

Mathematical modeling demonstrates that fractional memory effects and stochastic perturbations delay computer virus propagation in network populations, suggesting optimized cybersecurity control...

Key Points

To develop and evaluate a fractional-order mathematical model incorporating historical memory traces and stochastic noise to investigate computer virus propagation and optimal control strategies.
Formulated a four-compartment epidemiological model (susceptible, latently infected, breaking-out, and antivirus-capable) using Caputo fractional derivatives.
Conducted qualitative analyses for existence, uniqueness, boundedness, basic reproduction number ($R_0$), and forward transcritical bifurcation.
Performed stochastic simulations via the Euler–Maruyama method and applied Pontryagin’s Maximum Principle to evaluate optimal prevention, treatment, and screening strategies.
The basic reproduction number ($R_0$) acts as a sharp threshold parameter governing the global stability of virus-free and endemic steady states.
Numerical simulations showed that stronger memory effects (lower fractional orders) substantially delay the rate of virus transmission across the network.
Sensitivity analyses identified the virus transmission rate and latency transition rate as the most critical determinants of outbreak severity.

Cite This Study

Alhazmi et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b2f70926e14a848b196b https://doi.org/10.1142/s012918312642009x

View Full Paper

Ask AI

Bookmark