Outbreak‐BasedGiardiaDose–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 doseresponse 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.

Quantitative Microbial Risk Assessment for Contaminated Private Wells in the Fractured Dolomite Aquifer of Kewaunee County, Wisconsin

“…Models validated against (or developed with) observational epidemiological data were selected when available, because these models would be expected to represent wild-type pathogens and natural host populations (i.e., including a mix of "normal," immune, and highly susceptible hosts). Dose-response models selected for this reason included the models for all three Cryptosporidium categories and G. duodenalis (Burch 2019(Burch , 2020DuPont et al 1995;Messner et al 2001) as well as Teunis et al's (2008) EPEC model and the World Health Organization's Salmonella model (WHO 2002). In the absence of validated models, and in the presence of multiple alternative models, an attempt was made to select a model that predicted a moderate level of infectivity (i.e., for C. jejuni) (Medema et al 1996;Schmidt et al 2013).…”

“…Finally, for the logistic-normal Poisson with morbidity model, the basic underlying dose-response relationship is assumed to be exponential (with a morbidity ratio), as in Equation 2. However, to incorporate variability in natural populations, the parameters r inf and P illjinf are also assumed to follow logistic-normal distributions (Burch 2020):…”

Statewide Quantitative Microbial Risk Assessment for Waterborne Viruses, Bacteria, and Protozoa in Public Water Supply Wells in Minnesota

“…The primary criterion for parameter selection was that corresponding dose–response models were validated against or developed with observational epidemiological data. This provides some assurance that risk estimates represent wild-type pathogens and a natural mix of host populations, and it applies to models selected for C. parvum , 32 C. hominis , 33 , 34 Giardia duodenalis , 35 Shiga toxin 2-producing bacteria (modeled as EPEC), 36 , 37 and Salmonella spp. 37 , 38 Similarly, dose–response for Cryptosporidium spp.…”

“…parvum, C. hominis, , Giardia duodenalis, Shiga toxin 2-producing bacteria (modeled as EPEC), , and Salmonella spp. , Similarly, dose–response for Cryptosporidium spp. was based on combining output from the dose–response models for C.…”