Dinesafe Knowledge Base
  • Welcome
  • Return to DineSafe.com
  • Real-Time Norovirus Surveillance
    • Introduction
  • Methodology
  • Data Collection & Processing
  • Metrics & Indicators
  • Implementation Guidelines
  • FAQs
  • Benchmarking
    • Introduction
      • The Importance of Food Safety
      • Introduction to Our Benchmarking System and Its Purpose
      • Why Food Safety Benchmarking Matters: A Stakeholder Overview
    • Methodology & Systems
      • The Metrics We Use
        • Reports per 100 Stores
        • Reports per $1M Revenue
        • Persons Reported Sick per 100k Customers Served
      • Data Sources & Methodology
        • How We Gather Data
        • Estimating Data When Exact Figures Aren't Available
        • Ensuring Accuracy and Transparency
      • Our Benchmark Indexing System
        • Why We Use Indexing
        • Index Composition
        • Index Calculation Process
        • Understanding Index Comparisons
    • Case Study
      • Case Study - Chipotle Mexican Grill
    • Applications
      • Global Applicability of Our Benchmarking System
      • Product-Specific Benchmarking for Food Producers
    • Service Information
      • Why Subscribe to Our Benchmarking Service
      • Limitations & Considerations
      • Conclusion
      • FAQ
        • Q1: How often are the benchmarks updated?
        • Q2: Are the food poisoning reports reviewed before being included in the benchmarks?
        • Q3: How do you account for differences in restaurant size when comparing benchmarks?
        • Q4: Can restaurants submit their own data to improve the accuracy of the benchmarks?
        • Q5: How do your benchmarks relate to official health inspections and ratings?
        • Q6: How can businesses improve their benchmark scores?
  • Data Dictionary
    • Introduction
    • Primary Fields
    • Specialized Fields
    • Delivery Methods
    • Glossary
    • Further Questions
  • IWP Reporting Widget
    • Introduction
    • Why Our Widget? Key Features & Benefits
    • Embedding the Widget
      • How to Embed the Code
      • When to Use Simple Embedding Code vs Advanced Embedding Code
        • Benefits of the Advanced Embedding Code
        • Considerations When Using the Advanced Embedding Code
      • Test the Code
      • Standalone URL Option
      • Customization Options
      • Language Support
      • Mobile-Friendly Link
    • Data Management and Access
    • Compliance with FDA’s Voluntary National Retail Food Regulatory Program Standards
    • Examples of Embedded Widgets
    • FAQ
      • Q1: Who can use the Iwaspoisoned.com reporting widget and what is it?
      • Q2: How do I embed the widget on my website?
      • Q3: Is the widget customizable?
      • Q4: How do I access the reports?
      • Q5: How does the widget support FDA compliance?
  • Email Notifications
    • Introduction
    • Widget Email Alerts
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Methodology

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Methodology

Definition & Validation

The NoroProxy metric is defined as illness reports where symptoms include both vomiting and diarrhea concurrently. This specific symptom pattern has been validated as a reliable proxy for norovirus-like illness, representing approximately 30-50% of illness reports in comprehensive analyses.

Scientific Basis

The methodology is supported by epidemiological research demonstrating that the co-occurrence of vomiting and diarrhea has high specificity for norovirus infections compared to other common foodborne or communicable illnesses. While not diagnostic at the individual level, this pattern provides statistically significant population-level insights.

Correlation with Official Data

Excerpt from Infectious Disease Week Poster:

Additional Materials

IAFP presentation

Infectious Disease Week 2024 Poster:

3MB
IAFP 2024 - Correlating Self-Reported Norovirus-Like Illness with Epidemiological Data - Linkedin.pdf
pdf
796KB
ID Week Poster V4 JF Edits.10_14_2024.pdf
pdf
As shown in the chart above, the IWP and NoroSTAT datasets move in tandem: a Pearson correlation of 0.67 (p < 0.001) reveals a clear, straight-line link over time, and a Granger causality test (p = 0.003) shows that shifts in one dataset reliably predict shifts in the other. Together, these results demonstrate that real-time crowdsourced reports closely mirror official surveillance data—highlighting their promise for spotting norovirus outbreaks earlier.