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Modelling Optimal Control of In-Host HIV Dynamics Using Different Control Strategies.

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This study presents an optimal control model for Human Immunodeficiency Virus (HIV) treatment. The findings show that combined drug therapies significantly reduce viral load and transmission, highlighting the importance of protease inhibitors.

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Area of Science:

  • Mathematical modeling
  • Epidemiology
  • Public Health

Background:

  • Human Immunodeficiency Virus (HIV) remains a leading cause of mortality, particularly in Sub-Saharan Africa.
  • Effective treatment strategies are crucial for managing HIV infection and reducing transmission rates.

Purpose of the Study:

  • To develop and analyze an in vivo deterministic model for HIV dynamics.
  • To apply optimal control theory to identify effective HIV treatment strategies.
  • To investigate the role of different drug regimens, including protease inhibitors, in controlling HIV.

Main Methods:

  • Utilized a deterministic model of differential equations to simulate HIV dynamics.
  • Applied optimal control theory, specifically Pontryagin's Maximum Principle, to determine optimal treatment strategies.
  • Employed a numerical approach using the Runge-Kutta fourth-order scheme for solving the model.

Main Results:

  • Optimal controlled treatment strategies demonstrated a significant reduction in viral load.
  • The model indicated a substantial decrease in HIV transmission rates under optimal control.
  • Protease inhibitors were identified as playing a key role in virus suppression, especially when used in combination therapy.

Conclusions:

  • An optimal treatment strategy combining multiple drug regimens can effectively suppress HIV.
  • Mathematical modeling and optimal control theory provide valuable insights into designing effective HIV interventions.
  • Integrated treatment approaches are essential for maximizing the impact of HIV therapy and reducing disease burden.