Submitted to: Risk Analysis
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/2/2019
Publication Date: 4/21/2020
Citation: Burch, T.R. 2020. Outbreak-based Giardia dose-response model using Bayesian hierarchical Markov chain Monte Carlo analysis. Risk Analysis. 40(4):705-722. https://doi.org/10.1111/risa.13436.
Interpretive Summary: Giardia is a gastrointestinal parasite that infects both humans and animals, responsible for 300 million infections per year globally, including 1 million in the U.S. Giardia can be transmitted from livestock to humans in contaminated drinking water and recreational water, and quantitative microbial risk assessment (QMRA) can be used to estimate the extent of this transmission and the effectiveness of proposed public health interventions to prevent it. However, the mathematical model used by QMRA to describe interactions between Giardia and human hosts is out of date, because it ignores valuable information about Giardia infectivity that has come to light since the model’s original development. The current study has updated this model, and results indicate that the previous model overestimated the certainty with which host-pathogen interactions could be described for Giardia. Furthermore, the current study’s updated model includes a novel ability to incorporate variability in host-pathogen interactions into predictions for Giardia. These improved model predictions will enable more accurate forecasting of Giardia transmission from livestock facilities, as the pressures driving this transmission are likely to shift in the face of land-use change and continuing intensification of livestock agriculture.
Technical 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 2-dimensional Monte Carlo simulation, while potential over-reporting of outbreak cases was handled using a published over-reporting factor 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 sr = 5.2×10-1 [1.1×10-1, 9.6×10-1] and sm = 9.3×10-1 [7.0×10-2, 2.8×100], 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, and these may be of particular interest for practical risk characterization, because they avoid the need to evaluate problematic hypergeometric functions.