Abstract
Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health.
Highlights
Foodborne diseases have a considerable economic and public health impact
In the second part of this paper, we investigate the value of gravity models in the context of foodborne disease outbreak source investigation by clarifying: How can the shopping behavior of consumers contribute to identifying the contamination source of a foodborne disease outbreak?
We demonstrate this by evaluating the performance of the source estimator on two food supply networks: (A) a network model where food retailers are connected to consumers with a gravity model; and (B) a network model where consumers are assumed to shop only in their home zip code
Summary
While developing countries are most affected, foodborne diseases should be considered a global issue that concern all countries [1]. While most outbreaks occur locally and can be directly attributed to a contamination source, there are a few widespread outbreaks that affect larger geographical areas, making them difficult to resolve. The Listeria monocytogenes outbreak Sigma 1 exemplifies this kind of widespread distribution. In this outbreak, a total of 37 illnesses were reported across 12 different states in Germany. It took authorities six years to identify a meat producer as the contamination source [3,4]. Such long investigation times are common and often do not guarantee
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