title: Dose-response model for 13 strains of salmonella.
abstract: Data from a human feeding trial with healthy men were used to develop a dose-response model for 13 strains of Salmonella and to determine the effects of strain variation on the shape of the dose-response curve. Dose-response data for individual strains were fit to a three-phase linear model to determine minimum, median, and maximum illness doses, which were used to define Pert distributions in a computer simulation model. Pert distributions for illness dose of individual strains were combined in an Excel spreadsheet using a discrete distribution to model strain prevalence. In addition, a discrete distribution was used to model dose groups and thus create a model that simulated human feeding trials. During simulation of the model with @Risk, an illness dose and a dose consumed were randomly assigned to each consumption event in the simulated feeding trial and if the illness dose was greater than the dose consumed then the model predicted no illness, otherwise the model predicted that an illness would occur. To verify the dose-response model predictions, the original feeding trial was simulated. The dose-response model predicted a median of 69 (range of 43-101) illnesses compared to 74 in the original trial. Thus, its predictions were in agreement with the data used to develop it. However, predictions of the model are only valid for eggnog, healthy men, and the strains and doses of Salmonella used to develop it. When multiple strains of Salmonella were simulated together, the predicted dose-response curves were irregular in shape. Thus, the sigmoid shape of dose-response curves in feeding trials with one strain of Salmonella may not accurately reflect dose response in naturally contaminated food where multiple strains may be present.
authors.affiliation: Microbial Food Safety Research Unit, Agriculture Research Service, US Department of Agriculture, Center for Food Science and Technology, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA. toscar@umes.edu
authors.forename: Thomas
authors.initials: T
authors.lastname: Oscar
mesh_terms: D015169:Colony Count, Microbial; D023381:Endpoint Determination; D005516:Food Microbiology; D006801:Humans; D016014:Linear Models; D008297:Male; D008954:Models, Biological; D018570:Risk Assessment; D012475:Salmonella / Q000472:pathogenicity*; D012478:Salmonella Food Poisoning / Q000209:etiology* / Q000382:microbiology; D013045:Species Specificity