Microbial Dose-Response Curves and Disinfection Efficacy Models Revisited
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Microbial Dose-Response Curves and Disinfection Efficacy Models Revisited Micha Peleg 1 Received: 25 June 2020 / Accepted: 7 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The same term “dose-response curve” describes the relationship between the number of ingested microbes or their logarithm, and the probability of acute illness or death (type I), and between a disinfectant’s dose and the targeted microbe’s survival ratio (type II), akin to survival curves in thermal and non-thermal inactivation kinetics. The most common model of type I curves is the cumulative form of the beta-Poisson distribution which is sometimes indistinguishable from the lognormal or Weibull distribution. The most notable survival kinetics models in static disinfection are of the Chick-Watson-Hom’s kind. Their published dynamic versions, however, should be viewed with caution. A microbe population’s type II dose-response curve, static and dynamic, can be viewed as expressing an underlying spectrum of individual vulnerabilities (or resistances) to the particular disinfectant. Therefore, such a curve can be described mathematically by the flexible Weibull distribution, whose scale parameter is a function of the disinfectant’s intensity, temperature, and other factors. But where the survival ratio’s drop is so steep that the static dose-response curve resembles a step function, the Fermi distribution function becomes a suitable substitute. The utility of the CT (or Ct) concept primarily used in water disinfection is challenged on theoretical grounds and its limitations highlighted. It is suggested that stochastic models of microbial inactivation could be used to link the fates of individual viruses or bacteria to their manifestation in the survival curve’s shape. Although the emphasis is on viruses and bacteria, most of the discussion is relevant to fungi, protozoa, and perhaps worms too. Keywords Kinetics . Viruses . Bacteria . CT (or Ct) . Chick-Watson-Hom’s models . Distribution functions . Stochastic models . Survival models
Introduction COVID 19 [43, 45] transmission through consumption of contaminated food has not been an issue in the current crisis, but other kinds of viruses remain a health hazard [3, 10, 52]. Conventional food preservation methods, primarily targeting cellular organisms especially bacteria, have been apparently efficient in destroying viruses as well [4]. However, meatprocessing plants have recently emerged as hotspots in the pandemic spread in rural areas, and food service operations been identified as potential culprits of its spread among humans. Thus, not surprisingly, disinfection of personnel, produce, clothing, air, tools, equipment, or any surface that humans can be in contact with has recently become a common practice and increasingly mandated by health authorities. * Micha Peleg [email protected] 1
Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA
There is a very rich body of scientific and technical literature on disinfe
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