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Fractional-order mathematical model for Monkeypox transmission dynamics using the Atangana-Baleanu Caputo operator
Benedict Celestine Agbata1, Erjola Cenaj2, Raimonda Dervishi2
1Department of Mathematics and Statistics, Faculty of Science, Confluence University of Science and Technology, Osara, Nigeria. agbatacelestine92@gmail.com.
BMC Infectious Diseases
|August 9, 2025
Summary
This study models Monkeypox transmission using a novel fractional-order approach, accounting for reinfection. Findings show combined quarantine, treatment, and public health measures significantly curb Monkeypox spread.
Area of Science:
- Epidemiology
- Mathematical Biology
- Fractional Calculus
Background:
- Monkeypox outbreaks pose ongoing global health challenges.
- Traditional models often neglect reinfection and fractional-order dynamics, limiting accuracy.
- Accurate modeling is crucial for effective disease control strategies.
Purpose of the Study:
- To develop and analyze a fractional-order model for Monkeypox transmission dynamics.
- To incorporate the Atangana-Baleanu-Caputo fractional derivative with a Mittag-Leffler kernel.
- To investigate the impact of interventions and identify key disease spread parameters.
Main Methods:
- Application of the Atangana-Baleanu-Caputo fractional derivative.
- Utilizing the Picard-Lindelöf method for existence and uniqueness of solutions.
- Numerical simulations via MATLAB ODE45 and sensitivity analysis.
Main Results:
- Fractional-order model accurately captures disease dynamics, including memory effects.
- Combined interventions (quarantine, treatment, PPE, contact tracing, vaccination) significantly reduce Monkeypox spread.
- Sensitivity analysis identified critical parameters influencing disease transmission.
Conclusions:
- Fractional-order modeling offers superior representation of Monkeypox compared to integer-order models.
- Integrated public health strategies are vital for mitigating Monkeypox outbreaks.
- Enhanced preparedness and targeted interventions are recommended for future infectious disease threats.
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