[Application of the log-Logistic Model to Dose Response Relation in Microbial Risk Assessment]

Microbial risk assessment in food safety is a valuable tool to reduce the risks of infection by pathogens. The dose-response relation is aimed to establish the relationship between the dose of a pathogen that populations are exposed to and the probability of the adverse health effect by the pathogen. Among many dose-response models ever proposed, the exponential and beta-Poisson models have been internationally applied, but the decision on which model is selected between them solely depends on the goodness of fit to specific data sets. On the other hands, the log-logistic model, one of the alternative models, has been little studied on the dose-response relation. In the present study, thus, the application of the log-logistic model to dose-response relation was studied with hypothetical and experimental data sets of infection (or death), comparing to the above two models. Here the experimental data sets were for pathogenic organisms such as pathogenic Escherichia coli, Listeria monocytogenes, and Cryptosporidium pavrum. Consequently, this model successfully fit to those data sets in comparison to the two models. These results suggested that log-logistic model would have the potential to apply to the dose-response relation, similar to the exponential and beta-Poisson models.

Modeling the Dose Response Relationship of Waterborne Acanthamoeba

This study developed dose response models for determining the probability of eye or central nervous system infections from previously conducted studies using different strains of Acanthamoeba spp. The data were a result of animal experiments using mice and rats exposed corneally and intranasally to the pathogens. The corneal inoculations of Acanthamoeba isolate Ac 118 included varied amounts of Corynebacterium xerosis and were best fit by the exponential model. Virulence increased with higher levels of C. xerosis. The Acanthamoeba culbertsoni intranasal study with death as an endpoint of response was best fit by the beta-Poisson model. The HN-3 strain of A. castellanii was studied with an intranasal exposure and three different endpoints of response. For all three studies, the exponential model was the best fit. A model based on pooling data sets of the intranasal exposure and death endpoint resulted in an LD₅₀ of 19,357 amebae. The dose response models developed in this study are an important step towards characterizing the risk associated with free-living amoeba like Acanthamoeba in drinking water distribution systems. Understanding the human health risk posed by free-living amoeba will allow for quantitative microbial risk assessments that support building design decisions to minimize opportunities for pathogen growth and survival.