Research Article Assessing Uncertainty in A2 Respiratory Syncytial Virus Viral Dynamics Gilberto González-Parra 1,2 and Hana M. Dobrovolny 1 1 Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX 76132, USA 2 Grupo de Matem´ atica Multidisciplinar (GMM), Universidad de Los Andes, M´ erida 5101, Venezuela Correspondence should be addressed to Gilberto Gonz´ alez-Parra; gcarlos999@gmail.com Received 15 March 2015; Accepted 30 August 2015 Academic Editor: Fabien Crauste Copyright © 2015 G. Gonz´ alez-Parra and H. M. Dobrovolny. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Respiratory syncytial virus (RSV) is the most common cause of bronchiolitis and pneumonia in children younger than 1 year of age in the United States. Moreover, RSV is being recognized more ofen as a signifcant cause of respiratory illness in older adults. Although RSV has been studied both clinically and in vitro, a quantitative understanding of the infection dynamics is still lacking. In this paper, we study the efect of uncertainty in the main parameters of a viral kinetics model of RSV. We frst characterize the RSV replication cycle and extract parameter values by ftting the mathematical model to in vivo data from eight human subjects. We then use Monte Carlo numerical simulations to determine how uncertainty in the parameter values will afect model predictions. We fnd that uncertainty in the infection rate, eclipse phase duration, and infectious lifespan most afect the predicted dynamics of RSV. Tis study provides the frst estimate of in vivo RSV infection parameters, helping to quantify RSV dynamics. Our assessment of the efect of uncertainty will help guide future experimental design to obtain more precise parameter values. 1. Introduction Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract disease among infants, a frequent pathogen in elderly and immunosuppressed patients, and a major public health concern worldwide [1–5]. Healthy adults who contract RSV experience mild or asymptomatic infections, but it is the cause of 18% of hospitalizations due to pneumonia in adults older than 65 [6]. Tere is currently no vaccine for RSV and drug treatment is largely limited to treatment of symptoms and supportive care [7]. Tus it is crucial that we develop a detailed understanding of the viral kinetics of this disease. Historically, mathematical and computational methods have not played a large role in immunology and virology. Tis is now changing, and impressive advances have come from the use of simple models applied to the interpretation of quantitative data [8]. Mathematical models of viral infections help us quantify key parameters of the infection process [9– 12], optimize drug treatment regimens [13–16], and under- stand complex host-virus interactions [17–19]. Mathematical modeling is now commonly used to study HIV [20] and is becoming more common in other serious viral infections such as infuenza [21] and HCV [22]; it has not yet been used to investigate respiratory syncytial virus (RSV). Initial modeling studies for any virus ofen use a system of nonlinear diferential equations and the models are typ- ically ft to viral time course data to generate estimates of viral kinetic parameters. However, given the large amount of experimental error in viral titer measurements [23], parameter estimates vary widely and contain some degree of uncertainty. Uncertainty in model parameters can limit the predictive ability of the model, so it is important to understand how parameter uncertainty alters the predicted time course of the model. Uncertainty in diferential equations has been considered in recent decades in a wide variety of applied areas, such as physics, chemistry, biology, economics, sociology, and medicine [24–26]. Uncertainty in a diferential equation model can arise either through uncertainty in the initial conditions or through uncertainty in equation coefcients. In this paper uncertainty of both types is considered in Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2015, Article ID 567589, 9 pages http://dx.doi.org/10.1155/2015/567589