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

Water (Switzerland), ISSN: 2073-4441, Vol: 11, Issue: 10
2019
  • 11
    Citations
  • 0
    Usage
  • 31
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    11
    • Citation Indexes
      11
  • Captures
    31
  • Mentions
    1
    • Blog Mentions
      1
      • Blog
        1

Article Description

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.

Bibliographic Details

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

MDPI AG

Social Sciences; Biochemistry, Genetics and Molecular Biology; Agricultural and Biological Sciences; Environmental Science

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