Modeling the Dose Response Relationship of Waterborne Acanthamoeba

© 2020 Society for Risk Analysis..

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.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:41

Enthalten in:

Risk analysis : an official publication of the Society for Risk Analysis - 41(2021), 1 vom: 01. Jan., Seite 79-91

Sprache:

Englisch

Beteiligte Personen:

Dean, Kara [VerfasserIn]
Tamrakar, Sushil [VerfasserIn]
Huang, Yin [VerfasserIn]
Rose, Joan B [VerfasserIn]
Mitchell, Jade [VerfasserIn]

Links:

Volltext

Themen:

059QF0KO0R
Acanthamoeba
Beta-Poisson model
Dose response
Drinking water
Exponential model
Journal Article
Microbial risk assessment
Research Support, Non-U.S. Gov't
Water

Anmerkungen:

Date Completed 28.12.2021

Date Revised 28.12.2021

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1111/risa.13603

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM316175676