Abstract
Salmonella is among the most common food borne illnesses which may result from consumption of contaminated products. In this paper we model the co-occurrence data between USDA-controlled food processing establishments and various strains of Salmonella (serotypes) as a network which evolves over time. We apply a latent space model originally developed for dynamic analysis of social networks to predict the future link structure of the graph. Experimental results indicate predictive utility of analyzing establishments as a network of interconnected entities as opposed to modeling their risk independently of each other. The model can be used to predict occurrences of a particular strain of Salmonella in the future. That could potentially aid in proactive monitoring of establishments at risk, allowing for early intervention and mitigation of adverse consequences to public health.
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Sarkar, P., Chen, L., Dubrawski, A. (2008). Dynamic Network Model for Predicting Occurrences of Salmonella at Food Facilities. In: Zeng, D., Chen, H., Rolka, H., Lober, B. (eds) Biosurveillance and Biosecurity . BioSecure 2008. Lecture Notes in Computer Science(), vol 5354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89746-0_6
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DOI: https://doi.org/10.1007/978-3-540-89746-0_6
Publisher Name: Springer, Berlin, Heidelberg
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