Accelerating investigation of food-borne disease outbreaks using pro-active geospatial modeling of food supply chains

Over the last decades the globalization of trade has significantly altered the topology of food supply chains. Even though food-borne illness has been consistently on the decline, the hazardous impact of contamination events is larger [1-3]. Possible contaminants include pathogenic bacteria, viruses, parasites, toxins or chemicals. Contamination can occur accidentally, e.g. due to improper handling, preparation, or storage, or intentionally as the melamine milk crisis proved. To identify the source of a food-borne disease it is often necessary to reconstruct the food distribution networks spanning different distribution channels or product groups. The time needed to trace back the contamination source ranges from days to weeks and significantly influences the economic and public health impact of a disease outbreak. In this paper we describe a model-based approach designed to speed up the identification of a food-borne disease outbreak source. Further, we exploit the geospatial information of wholesaler-retailer food distribution networks limited to a given food type and apply a gravity model for food distribution from retailer to consumer. We present a likelihood framework that allows determining the likelihood of wholesale source(s) distributing contaminated food based on geo-coded case reports. The developed method is independent of the underlying food distribution kernel and thus particularly applicable to empirical distributions of food acquisition