Information-theoretic methods for food supply network identification in food-borne disease outbreaks

9 November 2021
14:00
Abstract

In the event of food-borne disease outbreaks, conventional epidemiological approaches to identify the causative food product are time-intensive and often inconclusive. Data-driven tools could help to reduce the number of products under suspicion by efficiently generating food-source hypotheses. We frame the problem of generating hypotheses about the food-source as one of identifying the source network from a set of food supply networks (e.g. vegetables, eggs) that most likely gave rise to the illness outbreak distribution over consumers at the terminal stage of the supply network. We introduce an information-theoretic measure that quantifies the degree to which an outbreak distribution can be explained by a supply network’s structure and allows comparison across networks. The method leverages a previously-developed food-borne contamination diffusion model and probability distribution for the source location in the supply chain, quantifying the amount of information in the probability distribution produced by a particular network-outbreak combination. We illustrate the method using supply network models from Germany and demonstrate its application potential for outbreak investigations through simulated outbreak scenarios and a retrospective analysis of a real-world outbreak.

 

Further information about the spearker:

Dr. Abigail Horn is a Research Associate in Biostatistics in the Department of Population and Public Health Sciences at the University of Southern California. The general area of her research is the combination of approaches from data science and systems modeling with emerging large-scale data sources to understand drivers of pressing public health and safety challenges that can inform future interventions. She has a Ph.D. from the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology (MIT), where she focused on modeling food supply systems from farm to fork for applications in food safety. Immediately following her Ph.D., she further developed the food system modeling work through a dual postdoctoral appointment at the German Federal Institute for Risk Assessment (BfR, Germany’s federal-level food safety research authority) and the Department of Transport Modeling and Policy at Kühne Logistics University (KLU) in Germany. Her current work involves using large-scale novel data streams to develop insights into spatial, temporal, and behavioral barriers to accessing nutritious food. She is also engaged in systems modeling of the COVID-19 epidemic in LA County (LAC) through direct engagement with the Los Angeles County Department of Public Health (LACDPH), to inform policy interventions that can help to reduce the disparate impacts of the epidemic. Her big-picture areas of research interest are the use of systems modeling and computational social science to (i) understand pathways contributing to and design interventions to prevent risky health behaviors, and (ii) investigate how food systems can be improved to meet convergent goals of improving nutrition equity, increasing resilience to food shocks, and improving resource sustainability.