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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.

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Abstract:The participants of the panel on education and training in Medical Informatics, concurred that health/medical informatics is today thriving as a separate discipline, despite inevitable uncertainties regarding the future. Conferees discussed the distinctions between physician-built systems and those designed by medical informaticians, focusing on methodology as critical to medical informatics.

In this paper we report the use of the open source Spatiotemporal Epidemiological Modeler (STEM, www.eclipse.org/stem) to compare three basic models for seasonal influenza transmission. The models are designed to test for possible differences between the seasonal transmission of influenza A and B. Model 1 assumes that the seasonality and magnitude of transmission do not vary between influenza A and B. Model 2 assumes that the magnitude of seasonal forcing (i.e., the maximum transmissibility), but not the background transmission or flu season length, differs between influenza A and B. Model 3 assumes that the magnitude of seasonal forcing, the background transmission, and flu season length all differ between strains. The models are all optimized using 10 years of surveillance data from 49 of 50 administrative divisions in Israel. Using a cross-validation technique, we compare the relative accuracy of the models and discuss the potential for prediction. We find that accounting for variation in transmission amplitude increases the predictive ability compared to the base. However, little improvement is obtained by allowing for further variation in the shape of the seasonal forcing function.