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16 results for “FSKX”
Figure 4 from: Sundermann EM, Nauta M, Swart A (2021) A ready-to-use dose-response model of Campylobacter jejuni implemented in the FSKX-standard. Food Modelling Journal 2: e63309. https://doi.org/10.3897/fmj.2.63309
Figure 4 The probability of illness and infection for the human population after consumption of Campylobacter jejuni-contaminated food. The probabilities are calculated based on the outbreak dataset with 1000 various mean doses (the so-called OutbreakVarMeanDoses simulation).
Supplementary material 1 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
SourceAttributionModel.fskx
Figure 6 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 6 Bar plot of number of human cases of Salmonella infection attributed to different sources. Subfigure A shows the result for the baseline data 2004–2007 (the so-called defaultSimulation in Table 2) and Subfigure B the results for themonitoring data 2010/2011 (the so-called SimulationTable3 in Table 2).
Figure 4 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 4 Starting points for the Markov chains of Parameter scenario 3 and their effect on the convergence behaviour and the model predictions. The starting points are concentrated near the points (0, 0.18) and (0.18, 0.18) (see the points in the scatter plot in the upper right corner). With this set of starting points the Markov chains do not converge within 30,000 iterations for the parameters \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} or \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_3 \end{equation*} \end{varwidth} \end{document} as can be seen in the four trace plots on the left hand side which show how the paramter values that the model estimates change through the iteration steps of the model calculations. Each of the four trace plots correspond to one model parameter (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} , \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_3\), \(q_2\) and \(q_3 \end{equation*} \end{varwidth} \end{document} , where types 2 and 3 correspond to S. enterica serotype Enteritidis PT 14b and PT 19, respectively). In each trace plot there are five traces, one trace for each Markov chain. Each Markov chain has its own colour). The error bars of the predicted source attribution are large (see the bar plot).
Figure 2 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 2 Starting points for the Markov chains of Parameter scenario 1, their effects on the convergence behaviour and the model predictions. The starting points are evenly spaced in the lower fifth of the space of possible starting points (see the points in the scatter plot in the upper right corner). With this set of starting points, Markov chains converge quickly as can be seen in the four trace plots on the left hand side which show how the paramter values that the model estimates change through the iteration steps of the model calculations. Each of the four trace plots correspond to one model parameter (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_1 \end{equation*} \end{varwidth} \end{document} , \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} , \(q_2\) \end{equation*} \end{varwidth} \end{document} and \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_3 \end{equation*} \end{varwidth} \end{document} , where types 1, 2 and 3 correspond to S. enterica serotype Enteritidis PT 11, PT 14b, and PT 19, respectively). In each trace plot there are five traces, one trace for each Markov chain. Each Markov chain has its own colour. The predicted source attribution shows small error bars (see the bar plot).
Figure 3 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 3 Starting points for the Markov chains of Parameter scenario 2 and their effects on the convergence behaviour and the model predictions. The starting points are concentrated near the points (0, 0) and (0.18, 0.18) (see the points in the scatter plot in the upper right corner). With this set of starting points, Markov chains converge slowly as can be seen in the four trace plots on the left hand side which show how the paramter values that the model estimates change through the iteration steps of the model calculations. Each of the four trace plots correspond to one model parameter (\documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_1 \end{equation*} \end{varwidth} \end{document} , \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_2 \end{equation*} \end{varwidth} \end{document} \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} , \(q_2\) and \(q_3\) \end{equation*} \end{varwidth} \end{document} , where types 1, 2 and 3 correspond to S. enterica serotype Enteritidis PT 11, PT 14b, and PT 19, respectively). In each trace plot there are five traces, one trace for each Markov chain. Each Markov chain has its own colour. The predicted source attribution shows small error bars (see the bar plot).
Figure 5 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 5 Model-data fit when setting \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} M_j \end{equation*} \end{varwidth} \end{document} =1 and using different parameterizations. Each point in the figure corresponds to one bacterial subtype. Subfigure A shows a consistent model fit due to using the prior \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a_j \sim uniform(0,30000) \end{equation*} \end{varwidth} \end{document} . I.e. the logarithm of the number of cases as found in the data corresponds well to the logarithm of predicted number of cases. Subfigure B shows an inconsistent model fit due to using the prior \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} a\sim uniform(0,20) \end{equation*} \end{varwidth} \end{document} . Here, the model systematically underestimates the number of cases for the subtypes as the points gather well below the identity line.
Figure 1 from: Sundermann EM, Correia Carreira G, Käsbohrer A (2021) An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls. Food Modelling Journal 2: e70008. https://doi.org/10.3897/fmj.2.70008
Figure 1 The posterior distributions for the fifth entry in the list of Salmonella subtypes (q5), which is S. enterica serotype Enteritidis PT 21, as a function of the possible values of q5. The shown posterior distributions are calculated by the Gibbs-Sampler software OpenBUGS using the Bayes DB model presented in Jabin et al. (2019) for the monitoring data and a sample size of 1e5. Subfigure A) shows the posterior distribution calculated for \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_i \sim uniform(0,0.2) \end{equation*} \end{varwidth} \end{document} . The chosen limits of the uniform distribution lead to a cut off in the posterior distribution. When enlarging the interval defining the prior distribution to \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} q_i \sim uniform(0,1) \end{equation*} \end{varwidth} \end{document} , then a complete posterior distribution is produced by OpenBUGS (see Subfigure B)).
Supplementary material 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
ChickenEgg-SCNM
Figure 3 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 3 Choropleth map for production of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model.
Figure 2 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 2 Choropleth map for total consumption of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model .
Figure 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 1 Simplified schematic representation of the food supply chain network in Germany illustrating actors (indicated by boxes) and transport processes (indicated by arrows) according to the dynamic freight flow model from Balster and Friedrich (2019). *Regarding Warehouse and Store: the respective 28 brands are implemented as aggregated and as individual actors in the "egg supply chain network model".
Figure 2 from: Sundermann EM, Nauta M, Swart A (2021) A ready-to-use dose-response model of Campylobacter jejuni implemented in the FSKX-standard. Food Modelling Journal 2: e63309. https://doi.org/10.3897/fmj.2.63309
Figure 2 The probability of illness and infection for the human population after consumption of a mean dose of 1 CFU of Campylobacter jejuni-contaminated food. The probabilities are calculated based on the outbreak dataset (the so-called outbreak simulation).
Figure 3 from: Sundermann EM, Nauta M, Swart A (2021) A ready-to-use dose-response model of Campylobacter jejuni implemented in the FSKX-standard. Food Modelling Journal 2: e63309. https://doi.org/10.3897/fmj.2.63309
Figure 3 The probability of illness and infection for the human population after consumption of Campylobacter jejuni-contaminated food. The probabilities are calculated based on the challenge studies dataset with 1000 various mean doses (the so-called ChallengeVarMeanDoses simulation).
Supplementary material 1 from: Sundermann EM, Nauta M, Swart A (2021) A ready-to-use dose-response model of Campylobacter jejuni implemented in the FSKX-standard. Food Modelling Journal 2: e63309. https://doi.org/10.3897/fmj.2.63309
CampylobacterDRM.fskx
Figure 1 from: Sundermann EM, Nauta M, Swart A (2021) A ready-to-use dose-response model of Campylobacter jejuni implemented in the FSKX-standard. Food Modelling Journal 2: e63309. https://doi.org/10.3897/fmj.2.63309
Figure 1 The probability of illness and infection for the human population after consumption of a mean dose of 1 CFU of Campylobacter jejuni contaminated food. The probabilities are calculated based on the challenge studies dataset (the so-called default simulation).
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International Brain Laboratory public data
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OpenNeuro
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