Microbial Dose-Response Curves and Disinfection Efficacy Models Revisited

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.

Terpenes modulate bacterial and fungal growth and sorghum rhizobiome communities

“…S6). While we may have expected that a higher concentration of linalool (200 µM) would have a greater impact on the microbial community compared to a lower concentration (100 µM), we found it particularly interesting that it was not the same taxa that were responding to the different levels indicating dose-responses differ between organisms and calling into question the distribution of such dose-response relationships ( 68 ). However, we did not expect that the sesquiterpene nerolidol would have fewer effects on the bacterial and fungal relative abundance in a community setting compared to the monoterpene treatments, as nerolidol is well known for its antibiotic activities ( 69 , 70 ).…”

Quantitative Assessment of Airborne Transmission of Human and Animal Influenza Viruses in the Ferret Model

“…The lognormal distribution for representing dose-response data is indistinguishable from the Poisson beta function for the most practical purposes. Mathematically, it can be shown that if the probability of infection (PN) is the product of the set of underlying probabilities of several events (Pis), i.e., P(N) = ∏ P(i), and if each of these underlying P(i) is uniformly distributed within its defined range, then the resulting probability of infection (PN) has a lognormal distribution [52,53]. The parameters of the lognormal distribution model are intuitive clear for researchers and can be measured experimentally.…”

High-throughput screening of natural compounds for prophage induction in controlling pathogenic bacteria in food

“…This is supported by the fact that the streptonigrin used as a positive control and in the dose response experiment was prepared as a fresh stock and resulted in a positive hit for prophage induction. Dose-response experiments are used to determine the response of an organism, to a stimulus or stressor, following exposure for a set exposure time (Peleg, 2021). In this study, "dose" is defined as the quantity of a compound that results in prophage induction.…”