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10:11
Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
Published on: September 27, 2014
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Modeling and Analysis of SIRR Model (Ebola Transmission Dynamics Model) with Delay Differential Equation
Akinleye Emmanuel Lasekan1, Joshua Oluwasegun Agbomola2, Kabir Oluwatobi Idowu3
1Department of Mathematics, Lagos State University, Ojo, Lagos, Nigeria.
F1000Research
|September 19, 2025
Summary
This study introduces a new Ebola virus disease model incorporating delays to better predict outbreaks. Increasing delays can cause oscillations and amplify infections, highlighting the need for delay-inclusive public health strategies.
Area of Science:
- Epidemiology
- Mathematical Biology
- Infectious Disease Modeling
Background:
- Ebola virus disease (EVD) is a severe, often fatal illness with high transmission potential and recurring outbreaks.
- Traditional compartmental models often neglect biologically important delays, limiting their ability to accurately capture real-world epidemic patterns.
- Including delays, such as the latent period, is crucial for understanding outbreak persistence and control.
Purpose of the Study:
- To develop and analyze a novel deterministic SIRR model for Ebola virus disease transmission dynamics.
- To explicitly combine nonlinear incidence rates with a delay differential equation framework.
- To investigate the influence of biologically motivated delays on epidemic patterns and stability.
Main Methods:
- Developed a deterministic SIRR model with nonlinear incidence and delay differential equations.
- Derived the basic reproduction number (R₀) using the next-generation matrix.
- Analyzed local stability, transcritical and Hopf bifurcations, and performed sensitivity analysis and numerical simulations.
Main Results:
- The model's stability depends on the basic reproduction number (R₀); disease-free equilibrium is stable for R₀<1, and endemic equilibrium emerges for R₀>1.
- Increasing delays destabilize the system, leading to amplified peak infections, prolonged outbreaks, and sustained oscillations.
- Isolation of recovered individuals (c) significantly reduces R₀; transmission rate (β), recruitment rate (Λ), and isolation transition rate (ρ) are key sensitive parameters.
Conclusions:
- Accounting for delayed recovery dynamics is crucial for accurate EVD outbreak prediction and intervention design.
- The delay-based, nonlinear-incidence model provides a robust framework for public health strategies.
- This approach has direct implications for reducing transmission, shortening outbreak duration, and preventing epidemic resurgence.
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