Next Article in Journal
Effect of Chinese Propolis as an Intracanal Medicament on Post-Operative Endodontic Pain: A Double-Blind Randomized Controlled Trial
Next Article in Special Issue
Pathogen-Specific Impacts of the 2011–2012 La Niña-Associated Floods on Enteric Infections in the MAL-ED Peru Cohort: A Comparative Interrupted Time Series Analysis
Previous Article in Journal
Health Literacy among People in Cardiac Rehabilitation: Associations with Participation and Health-Related Quality of Life in the Heart Skills Study in Denmark
Previous Article in Special Issue
Characterizing the Spatial Determinants and Prevention of Malaria in Kenya
Open AccessArticle

A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks

Transport Modeling, Kuehne Logistics University, 20457 Hamburg, Germany
Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
German Federal Institute for Risk Assessment (BfR), 12277 Berlin, Germany
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(2), 444;
Received: 24 December 2019 / Revised: 6 January 2020 / Accepted: 7 January 2020 / Published: 9 January 2020
(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)
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. View Full-Text
Keywords: gravity model; food supply network; food retailing; network source identification; epidemic; foodborne diseases gravity model; food supply network; food retailing; network source identification; epidemic; foodborne diseases
Show Figures

Figure 1

MDPI and ACS Style

Schlaich, T.; Horn, A.L.; Fuhrmann, M.; Friedrich, H. A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks. Int. J. Environ. Res. Public Health 2020, 17, 444.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop