Dynamic Network Model for Predicting Occurrences of Salmonella at Food Facilities

Authors:
Purnamrita Sarkar;Lujie Chen;Artur Dubrawski
Affiliations:
The Auton Lab, Carnegie Mellon University, Pittsburgh, USA PA 15213;The Auton Lab, Carnegie Mellon University, Pittsburgh, USA PA 15213;The Auton Lab, Carnegie Mellon University, Pittsburgh, USA PA 15213
Venue:
BioSecure '08 Proceedings of the 2008 International Workshop on Biosurveillance and Biosecurity
Year:
2008

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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.