Reducing Uncertainty in Within-Host Parameter Estimates of Influenza Infection by Measuring Both Infectious and Total Viral Load

Stephen M Petrie,James M Mccaw,Aeron C Hurt,Teagan Guarnaccia,Karen L Laurie,Jodie Mcvernon,Justin David Brown

doi:10.1371/journal.pone.0064098

Abstract

For in vivo studies of influenza dynamics where within-host measurements are fit with a mathematical model, infectivity assays (e.g. 50% tissue culture infectious dose; TCID50) are often used to estimate the infectious virion concentration over time. Less frequently, measurements of the total (infectious and non-infectious) viral particle concentration (obtained using real-time reverse transcription-polymerase chain reaction; rRT-PCR) have been used as an alternative to infectivity assays. We investigated the degree to which measuring both infectious (via TCID50) and total (via rRT-PCR) viral load allows within-host model parameters to be estimated with greater consistency and reduced uncertainty, compared with fitting to TCID50 data alone. We applied our models to viral load data from an experimental ferret infection study. Best-fit parameter estimates for the “dual-measurement” model are similar to those from the TCID50-only model, with greater consistency in best-fit estimates across different experiments, as well as reduced uncertainty in some parameter estimates. Our results also highlight how variation in TCID50 assay sensitivity and calibration may hinder model interpretation, as some parameter estimates systematically vary with known uncontrolled variations in the assay. Our techniques may aid in drawing stronger quantitative inferences from in vivo studies of influenza virus dynamics.

Highlights

Influenza is an infectious disease that causes significant morbidity and mortality worldwide [2]
We develop a mathematical model of influenza infection in ferrets, based on previous in vitro [12,33] and in vivo [3] models, and fit it to TCID50 and rRT-PCR data from experiments performed by Guarnaccia et al
We find that measurement of both infectious and total viral particle concentration allows some within-host model parameters to be estimated with reduced uncertainty – and with greater consistency in best-fit values across different experiments – when compared with parameter estimates obtained from fitting to infectious viral load data alone

Summary

Introduction

Influenza is an infectious disease that causes significant morbidity and mortality worldwide [2]. For in vivo studies of within-host influenza dynamics, infectivity assays such as 50% tissue culture infectious dose (TCID50) or plaque assays are often used as a measure of the infectious (viable) virion concentration over time [3,4,5,13,14,15,16,17,18,19] – we define infectious virions to be virions that can infect susceptible cells and initiate the production of progeny virus. In some in vivo influenza modelling studies [15,22,23,24], real-time reverse transcription-polymerase chain reaction (rRTPCR) assays that quantify viral RNA (vRNA) have been used as an alternative to infectivity assays – we define total (infectious and non-infectious) viral particles to be particles that contain vRNA measurable via rRT-PCR. Mathematical models that have been fitted to such total viral load data have implicitly assumed that the proportionality between infectious and total viral particle concentration is constant over time

Methods

Results

Conclusion