Modeling dynamics of acute HIV infection incorporating density-dependent cell death and multiplicity of infection

Understanding the dynamics of acute HIV infection can offer valuable insights into the early stages of viral behavior, potentially helping uncover various aspects of HIV pathogenesis. The standard viral dynamics model explains HIV viral dynamics during acute infection reasonably well. However, the model makes simplifying assumptions, neglecting some aspects of HIV infection. For instance, in the standard model, target cells are infected by a single HIV virion. Yet, cellular multiplicity of infection (MOI) may have considerable effects in pathogenesis and viral evolution. Further, when using the standard model, we take constant infected cell death rates, simplifying the dynamic immune responses. Here, we use four models—1) the standard viral dynamics model, 2) an alternate model incorporating cellular MOI, 3) a model assuming density-dependent death rate of infected cells and 4) a model combining (2) and (3)—to investigate acute infection dynamics in 43 people living with HIV very early after HIV exposure. We find that all models qualitatively describe the data, but none of the tested models is by itself the best to capture different kinds of heterogeneity. Instead, different models describe differing features of the dynamics more accurately. For example, while the standard viral dynamics model may be the most parsimonious across study participants by the corrected Akaike Information Criterion (AICc), we find that viral peaks are better explained by a model allowing for cellular MOI, using a linear regression analysis as analyzed by R2. These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Depending on the specific aspect of interest, a corresponding model should be employed.Author summary: We conducted a study to better understand the dynamics of early HIV-1 infection using four different mathematical models. These models tested various biological hypotheses, including the presence of cellular coinfection and phenomenological model capturing aspects of immune responses. We analyzed viral load data from 43 participants in a rich dataset of people recently infected. The models we used were the standard viral dynamics model, a model incorporating density-dependent cell death of infected cells, a model with coinfection of infected cells (an adaptation of a macroparasite model to HIV-1 dynamics), and a model that combined cellular coinfection and density-dependent cell death. Overall, the model incorporating density-dependent cell death of infected cells performed the best according to the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). We also assessed the models’ performance by comparing them based on measures such as viral growth rate, peak magnitude and timing, decay rate, and setpoint. We find that different models describe differing features of the dynamics more accurately. For example, viral peaks are better explained by a model allowing for cellular multiplicity of infection (MOI). These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Therefore, depending on the aspect of interest, a corresponding appropriate model should be employed.