Philip J. Schmidt

TL;DR: This research supports the use of QMRA as an effective source attribution tool when there is a lack of randomized controlled trial data to evaluate the public health risk of an exposure source and provides a framework for others to develop burden of waterborne illness estimates for small water supplies.

TL;DR: It was hypothesized that concurrent estimation of an unmeasured degree of virus aggregation and important dose‐response parameters could lead to structural nonidentifiability of the model, and this is demonstrated using the profile likelihood approach and by algebraic proof.

TL;DR: In this article, repeated rarefying is proposed as a tool to normalize library sizes for diversity analyses, which enables proportionate representation of all observed sequences and characterization of the random variation introduced to diversity analyses by rarefy to a smaller library size shared by all samples.

TL;DR: The theoretical origins of the beta-Poisson dose-response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions and developed MCMC procedures to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta-Proof dose- response models using Bayes's theorem and Markov Chain Monte Carlo.

TL;DR: This model is used to demonstrate that microorganism counts and analytical recovery are not independent (as has often been assumed), even if the correlation is obscured by other sources of variability in the data, and is implemented in a Bayesian framework to quantify temporal concentration variability.