Modelling long-term human immunodeficiency virus dynamic models with application to acquired immune deficiency syndrome clinical study

Abstract

Mathematical modelling of human immunodeficiency virus (HIV) dynamics has played an important role in acquired immune deficiency syndrome research. Deterministic dynamic models have been developed to study the viral dynamic process for understanding the pathogenesis of HIV type 1 infection and antiviral treatment strategies. We propose a new multistage estimation procedure which uses data, HIV viral load and CD4+ T-cell counts, from an acquired immune deficiency syndrome clinical study, to estimate the parameters in a long-term HIV dynamic model containing both constant and time varying parameters. Simulation studies and a real data application show that the methods proposed are efficient and appropriate to estimate both constant and time varying parameters in long-term HIV dynamic models. © 2010 Royal Statistical Society.