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Since the 2001 anthrax attack in the United States, awareness of threats originating from bioterrorism has grown. This led internationally to increased research efforts to improve knowledge of and approaches to protecting human and animal populations against the threat from such attacks. A collaborative effort in this context is the extension of the open-source Spatiotemporal Epidemiological Modeler (STEM) simulation and modeling software for agro- or bioterrorist crisis scenarios. STEM, originally designed to enable community-driven public health disease models and simulations, was extended with new features that enable integration of proprietary data as well as visualization of agent spread along supply and production chains. STEM now provides a fully developed open-source software infrastructure supporting critical modeling tasks such as ad hoc model generation, parameter estimation, simulation of scenario evolution, estimation of effects of mitigation or management measures, and documentation. This open-source software resource can be used free of charge. Additionally, STEM provides critical features like built-in worldwide data on administrative boundaries, transportation networks, or environmental conditions (eg, rainfall, temperature, elevation, vegetation). Users can easily combine their own confidential data with built-in public data to create customized models of desired resolution. STEM also supports collaborative and joint efforts in crisis situations by extended import and export functionalities. In this article we demonstrate specifically those new software features implemented to accomplish STEM application in agro- or bioterrorist crisis scenarios.

The Shiga toxin-producing Escherichia coli O104:H4 outbreak in Germany in 2011 required the development of appropriate tools in real-time for tracing suspicious foods along the supply chain, namely salad ingredients, sprouts, and seeds. Food commodities consumed at locations identified as most probable site of infection (outbreak clusters) were traced back in order to identify connections between different disease clusters via the supply chain of the foods. A newly developed relational database with integrated consistency and plausibility checks was used to collate these data for further analysis. Connections between suppliers, distributors, and producers were visualized in network graphs and geographic projections. Finally, this trace-back and trace-forward analysis led to the identification of sprouts produced by a horticultural farm in Lower Saxony as vehicle for the pathogen, and a specific lot of fenugreek seeds imported from Egypt as the most likely source of contamination. Network graphs have proven to be a powerful tool for summarizing and communicating complex trade relationships to various stake holders. The present article gives a detailed description of the newly developed tracing tools and recommendations for necessary requirements and improvements for future foodborne outbreak investigations.