Interactive visualization of the complete trace of an observed station.

<p>Outbreak stations are marked in (striped) red. After selecting a station to be “observed” (blue) the view immediately shows the whole trace of that station: the green/green striped stations and deliveries indicate the forward trace and turquoise denotes the backward trace. In the two stations receiving deliveries from the blue station, cross-contamination is also assumed (black stripes). Deliveries leaving the observed station arrive at two outbreak stations (red/green), but not at the third one (red). This figure can be reproduced by using the available sample data.</p

Collapsing many stations into one meta-station.

<p>The meta-station is a collapsed version of all stations from a certain country (collapse based on attribute “Country”) resulting in a common link (yellow), i.e. this collapsed station (the country) has traces to all outbreak stations (red) and therefore carries the maximum score of 1. This figure can be reproduced by using the available sample data.</p

Schematic description of the data structure in FoodChain-Lab.

<p>A) General principle of food supply chain reconstruction as performed by FoodChain-Lab. The connection between a delivery to one station to the lot of the following station is of major importance for tracing analysis. B) Detailed data structure used by FoodChain-Lab for storing food supply chain information. The most important attributes for detailed data analysis are highlighted in green.</p

EHEC outbreak 2011.

<p>All seven outbreak clusters in Germany and France (red) investigated in detail can be traced back to the source of the outbreak, which is the producer of fenugreek seeds in Egypt (yellow, the backtrace score calculated is 1). Numerous other stations without cases (no color) also received seeds from this producer. The automatically generated network view is synchronized with a geographical view based on maps from OpenStreetMap.</p

KNIME workflow “Tracing and Visualization”.

<p>The “Supply Chain Reader” node is used to read tracing information from the integrated FoodChain-Lab database. Data processing results are then provided to the “Tracing” node, which is able to perform specific tracing calculations. The “Tracing View” node is the main node for interactive data and tracing analysis. Finally the “GIS View” node can be used to create a geographical visualization of the food chain network using additional GIS information fed in via the “Shapefile Reader” node.</p

Simple basic visualization combining network and geographical view.

<p>Interactive trade network visualization: network graph (left) and GIS map view based on shapefiles (right). Stations on the left and on the right are identical and always synchronized, i.e. the blue stations are identical. In contrast to the GIS view, the graph view automatically groups stations that are connected via deliveries to demonstrate relationships between stations. This figure can be reproduced by using the available sample data.</p

Average success rate, sensitivity with 1/32 and 1/8 threshold, and average suspect product set size vs. number of case reports.

<p>Average success rate, sensitivity with 1/32 and 1/8 threshold, and average suspect product set size vs. number of case reports.</p

Number of case reports per location for several different outbreaks each generated based on a different product.

<p>For each product the results are averaged over 50 trials. For each trial, the x axis is sorted from most to least frequently occurring location to show the outbreak pattern.</p

Hierarchical clustering diagram of 580 food products.

<p>Different colors indicate different clusters, defined by a cut-off value of 0.25. (Note that colors were used multiple times, i.e., non-adjacent clusters of the same color are not related in any special way.)</p

A series of small images illustrating distribution patterns of food products sold by a German food retail company stratified by zip codes.

<p>For illustration purposes, all product clusters containing exactly three products are displayed. Clusters are arranged in two columns of seven clusters each. Other cluster sizes exhibit similar correlations between product distribution patterns. This image is published with permission from Esri and its data providers, and from Michael Bauer Research GmbH, Nürnberg, Germany; Data Source: Microm 2013.</p