HIV Infection

Machine Learning for Multiscale Analysis of Biomedical Data

HIV Infection

Persistence and the Implications for an HIV Cure

Overview: According to UNAIDS estimates for the year 2012, 35.3 million persons are infected with the HIV worldwide and there were 2.3 million new infections and 1.6 million deaths that year. HIV, therefore, remains one of the major health threats.  The cost associated with delivering antiretroviral drugs to people infected with HIV is overwhelming many organizations and public health systems worldwide.

State  of the art: At present, combined antiretroviral treatment (ART) potently suppresses the virus to undetectable levels in blood. Nevertheless, virus persistence within different reservoirs and compartments presents a major barrier to eradicate the virus in patients undergoing long-term antiviral combination therapy.

Our research in HIV infection aims to:

  • derive  mathematical models to decipher the  multifactorial events supporting HIV persistence

  • predict “shrink-kick-kill” strategies that can increase the probability of HIV infected patients becoming post-treatment controllers

  • derive  control strategies to  mitigate  viral mutations

Relevant Publications in this Field:

  • E.A. Hernandez-Vargas, J. Martinez-Picado, G. Hernandez-Mejia. Long-term impact of antiretroviral strategies for a functional HIV cure: A virtual clinical trial.  Proceedings in the 10th IFAC Symposium on Biological and Medical Systems, Sao Paulo, Brazil. 2018 

  • E.A. Hernandez-Vargas. Modeling Kick-Kill strategies towards HIV cure. Frontiers in Immunology. 2017 [PDF]

  • S. Jaafoura,M. G. de Goër de Herve, E. A. Hernandez-Vargas, H. Hendel-Chavez, M. Abdoh, M. C. Mateo, R. Krzysiek,M. Merad, R. Seng, M. Tardieu, J. F. Delfraissy, C. Goujard, and  Y. Taoufik,  Progressive contraction of the latent HIV reservoir around a core of less-differentiated ​CD4+ memory T Cells.  Nature Communications, Vol.5,  5407, 1-8, 2014 [PDF]

  • E.A. Hernandez-Vargas, P. Colaneri, and R.H. Middleton. Switching Strategies to Mitigate HIV Mutation. IEEE Transactions on Control Systems Technology,  2014 [PDF]


  • M. Haering, A. Hördt, M. Meyer-Hermann and E. A. Hernandez-Vargas. Computational study to determine when to initiate and alternate therapy in HIV infection. BioMed Research International, 2014 [PDF]

  • E.A. Hernandez-Vargas, P. Colaneri, and R. Middleton. Optimal therapy scheduling for a simplified HIV infection model. Automatica, Vol.49 (9), 2874–2880, 2013 [PDF]

  • E.A. Hernandez-Vargas, and R. Middleton. Modeling the three stages in HIV infection. Journal of Theoretical Biology, Vol. 320, 33-40, 2013 [PDF]

  • J. Ferreira, E.A. Hernandez-Vargas, and R. Middleton. Computer Simulation of Structured Treatment Interruption for HIV infection. Computers Methods and Programs in Biomedicine, Vol. 104 (2), 50-61 2011 [PDF]


  • E.A. Hernandez-Vargas, Dhagash Mehta, and R. Middleton. Towards modeling HIV long term behavior.  IFAC World Congress, Milan, Italy, 2011 [PDF]

  • E.A. Hernandez-Vargas, P. Colaneri, R. Middleton, and F. Blanchini. Discrete-time control for switched positive systems with application to mitigating viral escape. International Journal of Robust and Nonlinear Control, Vol. 21 (10), 1093–1111, 2010 [PDF]