Modeling the dynamics of biomarkers during primary HIV infection taking into account the uncertainty of infection date

TL;DR

This paper introduces a likelihood-based method to estimate parameters of HIV infection models, accounting for the uncertainty in infection dates, and applies it to a large European patient dataset.

Contribution

It presents a novel approach that incorporates infection date uncertainty into dynamical models of HIV, improving parameter estimation accuracy.

Findings

Effective estimation of model parameters despite unknown infection dates

Application to 761 patients demonstrates method's robustness

Enhanced understanding of biomarker dynamics during primary HIV infection

Abstract

During primary HIV infection, the kinetics of plasma virus concentrations and CD4+ cell counts is very complex. Parametric and nonparametric models have been suggested for fitting repeated measurements of these markers. Alternatively, mechanistic approaches based on ordinary differential equations have also been proposed. These latter models are constructed according to biological knowledge and take into account the complex nonlinear interactions between viruses and cells. However, estimating the parameters of these models is difficult. A main difficulty in the context of primary HIV infection is that the date of infection is generally unknown. For some patients, the date of last negative HIV test is available in addition to the date of first positive HIV test (seroconverters). In this paper we propose a likelihood-based method for estimating the parameters of dynamical models using a population approach and taking into account the uncertainty of the infection date. We applied this method to a sample of 761 HIV-infected patients from the Concerted Action on SeroConversion to AIDS and Death in Europe (CASCADE).