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Open AccessArticle

Predictive Water Virology: Hierarchical Bayesian Modeling for Estimating Virus Inactivation Curve

1
Department of Civil and Environmental Engineering, Tohoku University, Aoba 6-6-06, Aramaki, Aoba-ku, Sendai, Miyagi 980-8579, Japan
2
New Industry Creation Hatchery Center, Tohoku University, Aoba 6-6-10, Aramaki, Aoba-ku, Sendai, Miyagi 980-8579, Japan
3
Department of Frontier Science for Advanced Environment, Graduate School of Environmental Studies, Tohoku University, Aoba 6-6-06, Aramaki, Aoba-ku, Sendai, Miyagi 980-8579, Japan
*
Author to whom correspondence should be addressed.
Water 2019, 11(10), 2187; https://doi.org/10.3390/w11102187
Received: 30 August 2019 / Revised: 9 October 2019 / Accepted: 16 October 2019 / Published: 21 October 2019
Hazard analysis and critical control point (HACCP) are a series of actions to be taken to ensure product consumption safety. In food poisoning risk management, researchers in the field of predictive microbiology calculate the values that provide minimum stress (e.g., temperature and contact time in heating) for sufficient microbe inactivation based on mathematical models. HACCP has also been employed for health risk management in sanitation safety planning (SSP), but the application of predictive microbiology to water-related pathogens is difficult because the variety of pathogen types and the complex composition of the wastewater matrix does not allow us to make a simple mathematical model to predict inactivation efficiency. In this study, we performed a systematic review and meta-analysis to construct predictive inactivation curves using free chlorine for enteric viruses based on a hierarchical Bayesian model using parameters such as water quality. Our model considered uncertainty among virus disinfection tests and difference in genotype-dependent sensitivity of a virus to disinfectant. The proposed model makes it possible to identify critical disinfection stress capable of reducing virus concentration that is below the tolerable concentration to ensure human health. View Full-Text
Keywords: HACCP; sanitation safety planning; systematic review; enteric viruses; predictive inactivation curve; hierarchical Bayesian model HACCP; sanitation safety planning; systematic review; enteric viruses; predictive inactivation curve; hierarchical Bayesian model
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Kadoya, S.-S.; Nishimura, O.; Kato, H.; Sano, D. Predictive Water Virology: Hierarchical Bayesian Modeling for Estimating Virus Inactivation Curve. Water 2019, 11, 2187.

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