Predictive water virology: Hierarchical bayesian modeling for estimating virus inactivation curve

Syun suke Kadoya, Osamu Nishimura, Hiroyuki Kato, Daisuke Sano

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number2187
JournalWater (Switzerland)
Volume11
Issue number10
DOIs
Publication statusPublished - 2019 Oct 1

Keywords

  • Enteric viruses
  • HACCP
  • Hierarchical Bayesian model
  • Predictive inactivation curve
  • Sanitation safety planning
  • Systematic review

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

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