Title

Equitable Post-Disaster Food Distribution

Houston, TX

Houston, TX from MetroLab Network

Contact

PROJECT LEAD

Ioannis Kakadiaris

CONTACT INFO

  • ioannisk@uh.edu

THE IDEA

The Houston team implemented AI-SERVE, a decision-making platform for the Houston Food Bank to equitably allocate resources for food distribution in the wake of a disaster.

THE CHALLENGE

AI-SERVE ((Support for Equitable and Resilient Food Distribution During Extreme Weather Events) addresses the challenges of equitable distribution of food during disasters that disrupt critical infrastructure and strain supply-chain resources. Rapid-onset weather event disasters and slow-onset disasters, such as the COVID-19 pandemic, have squeezed the capacity of food bank disaster partners. Food banks shouldered much of the nutritional needs of families impacted by the pandemic while also continuing to prepare for hurricane season.


THE TEAM

Research partners:

  • University of Houston

Vanderbilt University

  • UTHealth Houston
  • Civic Partners:
  • Houston Food Bank

THE APPROACH

The team designed and implemented four modules to increase demand coverage and equitability.

  • Planning Module: deciding which disaster partners are needed for Hurricane Season to ensure the equitable distribution of food.
  • Preparedness Module: predicting which partners will be impacted by a weather event and making alternative plans for mobile distribution or recruiting additional partners.
  • Response Module: tracking which disaster partners have the ability to open and assessing their needs.
  • Recovery Module: monitoring disaster partner recovery from a weather event and the needs of their communities.

    • THE IMPACT

    • Situational awareness for different areas around Houston.
    • Tracks the availability of disaster partners in the most vulnerable areas.
    • Predicts flood risk for the disaster partners engaged in recovery operations.
    • Predicts infrastructure risk for disaster partners.
    • Helps prepare for a catastrophic event by predicting which partners will be able to participate in a recovery effort.
    • Communicating with all partners to see real-time status during recovery.