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Your search keyword '"Pirkko Tuominen"' showing total 2 results
Start Over Author "Pirkko Tuominen" Publisher mdpi ag
2 results on '"Pirkko Tuominen"'

1. BIKE: Dietary Exposure Model for Foodborne Microbiological and Chemical Hazards

Author

Jukka Ranta, Antti Mikkelä, Johanna Suomi, and Pirkko Tuominen

Subjects
exposure assessment, Bayesian, model, uncertainty, variability, foodborne hazard, Chemical technology, TP1-1185
Abstract

BIKE is a Bayesian dietary exposure assessment model for microbiological and chemical hazards. A graphical user interface was developed for running the model and inspecting the results. It is based on connected Bayesian hierarchical models, utilizing OpenBUGS and R in tandem. According to occurrence and consumption data given as inputs, a specific BUGS code is automatically written for running the Bayesian model in the background. The user interface is based on shiny app. Chronic and acute exposures are estimated for chemical and microbiological hazards, respectively. Uncertainty and variability in exposures are visualized, and a few optional model structures can be used. Simulated synthetic data are provided with BIKE for an example, resembling real occurrence and consumption data. BIKE is open source and available from github.

Published
2021
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2. BIKE: Dietary Exposure Model for Foodborne Microbiological and Chemical Hazards

Author

Johanna Suomi, Antti Mikkelä, Jukka Ranta, and Pirkko Tuominen

Subjects
foodborne hazard, Health (social science), chemical, exposure assessment, Computer science, Bayesian probability, education, Plant Science, TP1-1185, Bayesian inference, computer.software_genre, Health Professions (miscellaneous), Microbiology, Bayesian, Article, Synthetic data, Code (cryptography), uncertainty, Exposure assessment, Graphical user interface, microbiological, model, Dietary exposure, business.industry, variability, Chemical technology, ComputingMethodologies_PATTERNRECOGNITION, dietary, Data mining, User interface, business, computer, Food Science
Abstract

BIKE is a Bayesian dietary exposure assessment model for microbiological and chemical hazards. A graphical user interface was developed for running the model and inspecting the results. It is based on connected Bayesian hierarchical models, utilizing OpenBUGS and R in tandem. According to occurrence and consumption data given as inputs, a specific BUGS code is automatically written for running the Bayesian model in the background. The user interface is based on shiny app. Chronic and acute exposures are estimated for chemical and microbiological hazards, respectively. Uncertainty and variability in exposures are visualized, and a few optional model structures can be used. Simulated synthetic data are provided with BIKE for an example, resembling real occurrence and consumption data. BIKE is open source and available from github.

Published
2021

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