Foodborne disease outbreaks of recent years demonstrate that due to increasingly interconnected supply chains these type of crisis situations have the potential to affect thousands of people, leading to significant healthcare costs, loss of revenue for food companies, and--in the worst cases--death. When a disease outbreak is detected, identifying the contaminated food quickly is vital to minimize suffering and limit economic losses. Here we present a likelihood-based approach that has the potential to accelerate the time needed to identify possibly contaminated food products, which is based on exploitation of food products sales data and the distribution of foodborne illness case reports. Using a real world food sales data set and artificially generated outbreak scenarios, we show that this method performs very well for contamination scenarios originating from a single "guilty" food product. As it is neither always possible nor necessary to identify the single offending product, the method has been extended such that it can be used as a binary classifier. With this extension it is possible to generate a set of potentially "guilty" products that contains the real outbreak source with very high accuracy. Furthermore we explore the patterns of food distributions that lead to "hard-to-identify" foods, the possibility of identifying these food groups a priori, and the extent to which the likelihood-based method can be used to quantify uncertainty. We find that high spatial correlation of sales data between products may be a useful indicator for "hard-to-identify" products.

This paper describes the Spatiotemporal Epidemiological Modeler (STEM), an open source disease modeling application available through the Eclipse Foundation. The most distinguishing aspect of STEM is that it provides an open platform for researchers to build, run, share, and reuse models of infectious disease. We give a motivation why we believe an extensible architecture is desirable for these types of applications, allowing new models to be built on top of existing proven models. We also describe some of the features in STEM, and explain how GIS data must be extended to support interactions between regions -- including the mass action terms that drive transmission of infectious disease.

Tracking the location of people, computers, vehicles, and other mobile objects and performing computation on such location data are quickly gaining interest. The idea of location-based services has captured the imagination of application developers, wireless service providers, and content providers alike. However, the location of people, the most interesting mobile object, is tricky to determine because we don't track persons but rather the devices they carry or the transportation they use. That a person may be associated with numerous tracking devices simultaneously, but perhaps intermittently, makes location tracking challenging. We describe a methodology for aggregating location data from multiple sources associated with a single mobile object. We describe our existing location service infrastructure and illustrate scenarios where location aggregation is desirable and indeed required. Our experimentation in location-based services uses location data from wireless PDAs, vehicle tracking devices, and mobile and stationary computers.

The information age has brought with it the promise of unprecedented economic growth based on the efficiencies made possible by new technology. This same greater efficiency has left society with less and less time to adapt to technological progress. Perhaps the greatest cost of this progress is the threat to privacy we all face from unconstrained exchange of our personal information. In response to this threat, the World Wide Web Consortium has introduced the "Platform for Privacy Preferences" (P3P) to allow sites to express policies in machine-readable form and to expose these policies to site visitors [1]. However, today P3P does not protect the privacy of individuals, nor does its implementation empower communities or groups to negotiate and establish standards of behavior. We propose a privacy architecture we call the Social Contract Core (SCC), designed to speed the establishment of new "Social Contracts" needed to protect private data. The goal of SCC is to empower communities, speed the "socialization" of new technology, and encourage the rapid access to, and exchange of, information. Addressing these issues is essential, we feel, to both liberty and economic prosperity in the information age[2].

Tempus Fugit (Time Flies) is the first of a new generation of Personal Information Management (PIM) systems. A PIM system incorporates an electronic calendar, list and address book. The premise behind Tempus Fugit is that information stored in electronic calendars, to-do lists and address books can be given richer semantic interpretation and automatically processed to make its users more effective. Tempus Fugit also tracks the physical and virtual locations of users and uses this information to predict meeting attendance and help them as they travel during their day.