How robust are estimates of key parameters in standard viral dynamic models?

Journal Article · 16 April 2024 · PLoS Computational Biology (Online)

DOI: https://doi.org/10.1371/journal.pcbi.1011437 · OSTI ID:2341340

Zitzmann, Carolin ^[1]; Ke, Ruian ^[1]; Ribeiro, Ruy M. ^[1]; Perelson, Alan S.  ^[1]

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1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; National Institutes of Health (NIH)
• Grant/Contract Number: 89233218CNA000001; 20200743ER; 20200695ER; R01-AI078881; R01-AI152703; U54-HL143541
• OSTI ID: 2341340
• Alternate ID(s): OSTI ID: 2337720; OSTI ID: 2406571
• Report Number(s): LA-UR-23-29487
• Journal Information: PLoS Computational Biology (Online), Vol. 20, Issue 4; ISSN 1553-7358
• Publisher: Public Library of ScienceCopyright Statement
• Country of Publication: United States
• Language: English