AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It is demonstrated that fractional dynamics fundamentally reshape transient epidemic evolution, providing new insight into memory-driven processes in complex biological systems and nonlinear epidemiological modeling.
Despite vector control efforts, Nigeria remains highly endemic for malaria. Classical epidemiological models ignore both environmental variability and immunological memory, and these two factors critical to malaria transmission dynamics. This study introduces a novel mixed fractional-order stochastic malaria model inco...
Empirical results based on monthly influenza data from Xinjiang show that the proposed framework can capture epidemic trends, seasonal peaks, and fitting uncertainty and provide a theoretically grounded and practically applicable approach for infectious disease modeling under memory effects and stochastic perturbations...
A reduced deterministic SIR model in which a scalaraggregate viral-load variable modifies both the transmission rate and the rate of additional isolation, intended as a mathematically tractable bridge between classical compartmental models and detailed within-host–between-host simulations.
The analysis proves the absence of Hopf bifurcations, thereby excluding the existence of limit cycles and sustained oscillations, and provides a rigorous global description of the system and contributes to the qualitative theory of nonlinear epidemic models.
The results obtained from the proposed model suggest that combining behavioral awareness strategies with conventional public health interventions can substantially improve the long-term control of mpox.
This study proposes a susceptible-infectious-recovered (SIR) network model that incorporates a general recovery rate and a saturation treatment function, and establishes the well-posedness and global stability of the disease-free equilibrium in the model by employing semigroup theory and the standard comparison princip...
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