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Entropy-based analysis of Rift Valley fever transmission dynamics using delay differential equations.

Ali Raza1,2, Mansoor Alsulami3, Marek Lampart1

  • 1IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic.

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|February 5, 2026
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Summary
This summary is machine-generated.

Rift Valley Fever (RVF) is a zoonotic disease impacting livestock and humans. This study developed a mathematical model to understand RVF transmission dynamics, revealing critical factors influencing its spread and persistence.

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Veterinary Science

Background:

  • Rift Valley Fever (RVF) is a significant zoonotic disease transmitted by vectors like mosquitoes.
  • Outbreaks cause high mortality in livestock and severe illness in humans, leading to substantial economic losses.
  • Understanding RVF transmission dynamics is crucial for effective control strategies.

Purpose of the Study:

  • To investigate the transmission dynamics of Rift Valley Fever (RVF) using a deterministic epidemic model.
  • To incorporate delay differential equations to capture disease progression over time.
  • To analyze the stability and sensitivity of the model to identify key transmission parameters.

Main Methods:

  • Developed a deterministic epidemic model for RVF, dividing populations into susceptible, vaccinated, infected, and recovered compartments.
  • Utilized delay differential equations and an entropy-based global stability approach.
  • Derived the basic reproduction number and applied Routh-Hurwitz criteria and LaSalle's invariance principle for stability analysis.
  • Performed sensitivity analysis and numerical simulations using MATLAB's DDE23 solver.

Main Results:

  • Established basic dynamical properties (positivity and boundedness) of the model.
  • Identified and compared two equilibria: Rift Valley Fever-Free Equilibrium (RVFFE) and Rift Valley Fever Endemic Equilibrium (RVFEE).
  • Sensitivity analysis highlighted critical parameters affecting RVF transmission and persistence.
  • Numerical simulations demonstrated the impact of delay on infection dynamics and susceptibility.

Conclusions:

  • The study provides insights into the role of temporal factors and parameter uncertainty in RVF transmission.
  • The developed model aids in understanding the complex dynamics of this emerging zoonotic disease.
  • Findings can inform the development of improved control and prevention strategies for Rift Valley Fever.