Model-free based control of a HIV/AIDS prevention model.

Area of Science:

• Epidemiology
• Control Theory
• Public Health

Background:

• Optimal control theory is standard for epidemiological models but requires explicit system equations.
• Real-time control without prior system knowledge is a significant challenge in public health interventions.

Purpose of the Study:

• To develop and evaluate a model-free control algorithm for real-time management of HIV spread.
• To minimize the number of HIV-infected individuals using pre-exposure prophylaxis (PrEP) while respecting implementation constraints.

Main Methods:

• A model-free control algorithm operating in real-time, using direct feedback on the state solution.
• Application to an HIV epidemic model, controlling the supply of PrEP to susceptible individuals.
• Comparison with classical optimal control methods using Pontryagin's maximum principle.

Main Results:

• The model-free algorithm successfully reduced HIV-infected individuals asymptotically.
• It maintained the number of individuals on PrEP below a fixed constant value.
• Numerical simulations demonstrated competitive and novel performances compared to classical optimal control.

Conclusions:

• Model-free control offers an efficient alternative for real-time epidemiological management, particularly when system dynamics are unknown.
• This approach provides a flexible framework for optimizing public health interventions like PrEP distribution.
• The strategy shows promise for dynamic and adaptive control in infectious disease mitigation.