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Related Experiment Video

Updated: Sep 11, 2025

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A gradient-based time-delay optimization algorithm for evaluating control strategies in a fractional-order infectious

Indranil Ghosh1, Huey Tyng Cheong1, Kok Lay Teo1

  • 1School of Mathematical Sciences, Sunway University, 47500 Selangor Darul Ehsan, Malaysia.

Computers in Biology and Medicine
|August 15, 2025
PubMed
Summary

This study optimizes infectious disease control strategies using a fractional-order model, achieving significant cost reductions. The findings demonstrate the financial benefits of implementing targeted interventions and highlight the importance of memory effects in disease modeling.

Keywords:
Adams–Bashforth schemeCaputo–Fabrizio derivativeFractional optimal control problemGradient-based optimization algorithmSIDARTHE modelTime-delay

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Control Theory

Background:

  • Mathematical models are vital for disease control strategy development.
  • Optimizing cost-effective interventions remains a significant challenge.
  • Existing models often lack realistic cost functions and time-dependent parameter optimization.

Purpose of the Study:

  • To refine the SIDARTHE model with cost and control functions for optimal intervention strategies.
  • To develop and apply a novel gradient-based time-delay optimization algorithm for cost reduction.
  • To investigate the impact of fractional-order derivatives and memory properties on disease modeling.

Main Methods:

  • Refinement of the eight-compartmental SIDARTHE model with cost and control functions.
  • Application of Adams-Bashforth method and composite trapezoidal rule for optimization.
  • Implementation of Caputo-Fabrizio fractional derivatives to model disease dynamics.

Main Results:

  • Achieved an approximate 35.61% cost reduction through optimized control interventions.
  • Demonstrated the financial benefits of implementing control strategies.
  • Showcased improved model accuracy using Caputo-Fabrizio fractional derivatives, particularly for fractional order 0.7, matching Italian disease data.

Conclusions:

  • The developed time-delay optimization algorithm effectively reduces costs associated with disease control.
  • Fractional-order modeling, especially with Caputo-Fabrizio derivatives, enhances accuracy by capturing memory effects.
  • The study provides a foundation for future research in optimizing infectious disease treatment strategies.