AI vs. Outbreaks: Using Power BI to Predict Food Safety Failures Before They Happen
By Brad Smith, Data Smith Consultants
Every year, food contamination sickens millions and racks up billions in costs. But imagine if we could simulate outbreaks before they escalate... What if we could test how changes in policy, inspections, or supply chain practices shift the outcome?
That’s exactly what the PAIR Model does and it’s now built into an interactive Power BI dashboard ready for hands-on exploration.
⚙️ What Is the PAIR Model?
The Pathogen Alert and Intervention Response (PAIR) model is a simulation framework designed to help food safety professionals, regulators, and public agencies assess how the speed and strength of outbreak responses affect both public health and economic impact.
It combines:
🧠 AI-driven detection algorithms
🧮 Epidemiological modeling (SIERD)
🧑🌾 Simulations of behavior across the food chain (Regulators, Producers, Consumers)
💸 Cost modeling (including healthcare, recalls, legal liabilities)
Think of it as running policy fire drills for food safety before any real outbreak hits.
💡 Why Power BI?
Power BI transforms model outputs into intuitive policy dashboards, letting analysts, agency heads, or safety officers interact with:
Detection delays (24–144 hours)
Regulatory scenarios (PAIR Policy, FSMA Baseline, No Federal Testing)
Pathogen profiles (Listeria, Salmonella, E. coli)
And instantly visualize changes in:
No coding needed. Just click through and explore the outcomes.
🖥 Dive Into the Simulation
Inside, you’ll find:
📊 Overview Page: Side-by-side comparison of policy strategies
🔍 Scenario Drilldowns: Tweak variables like response time or containment rate
🧪 Pathogen Switcher: See how Listeria, E. coli, and Salmonella behave differently
📉 Outbreak Curves: Weekly trends showing rise and containment
🧬 How It Works (Skip the Heavy Math)
The PAIR model includes:
A SIERD model for disease progression (Susceptible, Exposed, Infected, Recovered, Deceased)
A QMRA engine to estimate dose-response infection risks
Agent-based behavior models for food system stakeholders
A Monte Carlo engine running 10,000 scenarios to provide probability ranges
It’s all under the hood so you can focus on understanding how smarter policies affect outcomes.
🚨 Why It Matters
In a simulated Listeria outbreak across the U.S. dairy supply chain, results showed:
A 45% drop in hospitalizations with AI detection in place
A $3.2 billion reduction in total costs
A 173% increase in costs under deregulation
This isn’t just a thought experiment, it’s a real-world decision-making tool.
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