Colloquium: The mechanistic Analysis of Founder Virus Data in Challenge Models
Event Description:
Time: Apr 16, 2021 03:30 PM Mountain Time (US and Canada)
Join Zoom Meeting
https://us02web.zoom.us/j/86275525234
Meeting ID: 862 7552 5234
Passcode: 016572
Abstract:
Repeated low-dose (RLD) challenge studies provide valuable information when evaluating candidate vaccines since they resemble the typical exposure of natural transmission and inform on the number of exposures prior to infection. This work uses the number of infecting viruses at the time of infection (rather than the occurrence of infection) to characterize candidate vaccine’s protective effect, by determining the vaccine's mechanism of action. We consider a null mechanism (no protection), a Leaky mechanism in which the number of infecting viruses is reduced by some factor in vaccinated subjects, the all-or-none mechanism in which the vaccine either offers complete protection or no protection in vaccinated subjects and a Combination model with both Leaky and All-or-none mechanisms. We consider two discrete marked survival models where the number of founder viruses follows a Poisson distribution with either a fixed mean parameter (Poisson model), or a random mean parameter that follows a Gamma distribution (Negative Binomial Model). We illustrate the performance of these methodologies with a data example of SIV on non-human primates and a simulation study.
Bio: Dr. Ana Maria Ortega-Villa is a Mathematical Statistician at the National Institute of Allergy and Infectious Diseases and an adjunct professor for the Data Analytics and Applied Statistic program at Virginia Tech. Prior to joining NIAID Dr. Ortega-Villa completed a postdoctoral fellowship at two institutes within NIH, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, and the National Cancer Institute. Dr. Ortega-Villa's research involves statistical methodology development motivated by allergy and infectious disease problems in fields including but not limited to longitudinal modeling, nonparametric and semiparametric statistics, and survival analysis.
