Food System Design for Resilient Population Health: The Minnesota Model

Despite being a world concern, food safety is addressed in a systematic way only in some developed countries. We need an integrated approach to managing the global food system that considers multiple needs and constraints, including population health, as well as an efficient system for transporting food with a rapid and accurate contamination surveillance system. Surveillance can help stop the spread of contamination, detect its source and change pathways for the particular food.

Knowledge about food safety and defense derived from a variety of fields can be brought together into a quantitative model for food protection. Specifically, we aim to define supply chain network features that simplify the complexity of the food chain — foodborne epidemiology, trade, the interdependencies of a specific network — and extrapolate early warning signals useful for supply chain design and surveillance systems able to rapidly detect food supply contamination outbreaks and their sources. The overarching goal is the integrated transdisciplinary design for the food system that considers population health endpoints related to environment- and industry-driven contamination and long-term health outcomes (e.g., nutrition and obesity). A complementary goal is to promote complex systems science approaches to the food system via participatory computing and collaboration to create a new efficient systems model.

This project aims to:

  • map endemic and critical paths of the food trade network for food safety, security and long-term health outcomes
  • determine changes in food trade, safety, potential population health outcomes and food industry payoff in the face of climate and population variations
  • develop novel biologically based multipathogen models (e.g., models that consider the incubation period of infections) that use ‘’social sensors’’ data (e.g., Twitter), epidemiological data and microbiological data for rapid detection of food contamination and outbreaks
  • predict foodborne outbreaks via metacommunity models as a function of socio-environmental and supply chain information, rapid outbreak source detection and use of social sensors
  • develop “applets’’ for social computing involving food system stakeholders in place of social sensors. 

Project Lead

  • Matteo Convertino (, Assistant Professor, Environmental Health Sciences