Outbreak‐Based Giardia Dose–Response Model Using Bayesian Hierarchical Markov Chain Monte Carlo Analysis

Abstract Giardia is a zoonotic gastrointestinal parasite responsible for a substantial global public health burden, and quantitative microbial risk assessment (QMRA) is often used to forecast and manage this burden. QMRA requires dose–response models to extrapolate available dose–response data, but the existing model for Giardia ignores valuable dose–response information, particularly data from several well‐documented waterborne outbreaks of giardiasis. The current study updates Giardia dose–response modeling by synthesizing all available data from outbreaks and experimental studies using a Bayesian random effects dose–response model. For outbreaks, mean doses ( D ) and the degree of spatial and temporal aggregation among cysts were estimated using exposure assessment implemented via two‐dimensional Monte Carlo simulation, while potential overreporting of outbreak cases was handled using published overreporting factors and censored binomial regression. Parameter estimation was by Markov chain Monte Carlo simulation and indicated that a typical exponential dose–response parameter for Giardia is r = 1.6 × 10 −2 [3.7 × 10 −3 , 6.2 × 10 −2 ] (posterior median [95% credible interval]), while a typical morbidity ratio is m = 3.8 × 10 −1 [2.3 × 10 −1 , 5.5 × 10 −1 ]. Corresponding (logistic‐scale) variance components were σ r = 5.2 × 10 −1 [1.1 × 10 −1 , 9.6 × 10 −1 ] and σ m = 9.3 × 10 −1 [7.0 × 10 −2 , 2.8 × 10 0 ], indicating substantial variation in the Giardia dose–response relationship. Compared to the existing Giardia dose–response model, the current study provides more representative estimation of uncertainty in r and novel quantification of its natural variability. Several options for incorporating variability in r (and m ) into QMRA predictions are discussed, including incorporation via Monte Carlo simulation as well as evaluation of the current study's model using the approximate beta‐Poisson.