Comparison of Risk Predicted by Multiple Norovirus Dose–Response Models and Implications for Quantitative Microbial Risk Assessment. Issue 2 (10th June 2016)

• Record Type: Journal Article
• Authors: Van Abel, Nicole; Schoen, Mary E.; Kissel, John C.; Meschke, J. Scott
• Is Part Of: Risk analysis. Volume 37:Issue 2(2017)
• Page Start: 245
• Page End: 264
• Publication Date: 2016-06-10
• Subjects: Dose response -- norovirus -- QMRA -- risk assessment; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.12616
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract : The application of quantitative microbial risk assessments (QMRAs) to understand and mitigate risks associated with norovirus is increasingly common as there is a high frequency of outbreaks worldwide. A key component of QMRA is the dose–response analysis, which is the mathematical characterization of the association between dose and outcome. For Norovirus, multiple dose–response models are available that assume either a disaggregated or an aggregated intake dose. This work reviewed the dose–response models currently used in QMRA, and compared predicted risks from waterborne exposures (recreational and drinking) using all available dose–response models. The results found that the majority of published QMRAs of norovirus use the1 F 1 hypergeometric dose–response model with α = 0.04, β = 0.055. This dose–response model predicted relatively high risk estimates compared to other dose–response models for doses in the range of 1–1, 000 genomic equivalent copies. The difference in predicted risk among dose–response models was largest for small doses, which has implications for drinking water QMRAs where the concentration of norovirus is low. Based on the review, a set of best practices was proposed to encourage the careful consideration and reporting of important assumptions in the selection and use of dose–response models in QMRA of norovirus. Finally, in the absence of one best norovirus dose–response model, multiple models should be used to provide a range of predicted outcomes for probability of infection.