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Quasi-metagenomic Analysis of Salmonella from Food and Environmental Samples
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Factors Influencing Foodborne Illness Self-Identification From User-Generated Data - Minnesota, 2024.

Lynette L Krampf1, Craig W Hedberg2, Brett Hauber3

  • 1Concordia University - Wisconsin, School of Education, 12800 N. Lake Shore Drive, Mequon, WI 53097, USA.

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|September 4, 2025
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People are more likely to report illness during publicized foodborne events when more people are affected or symptoms are listed. This impacts early outbreak detection using public data.

Keywords:
CrowdsourcingFoodborne illnessParticipatory epidemiologyUser-generated

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Area of Science:

  • Public health surveillance
  • Behavioral science
  • Data analytics

Background:

  • Foodborne illness outbreaks pose significant public health risks.
  • Rapid identification of outbreaks is crucial for implementing control measures.
  • Emerging data streams, including user-generated content and AI, offer potential for early outbreak detection.

Purpose of the Study:

  • To investigate factors influencing self-reporting of illness during publicized foodborne events.
  • To understand individual responses to varying outbreak information.
  • To assess the reliability of user-generated data for foodborne illness surveillance.

Main Methods:

  • A vignette-based discrete choice experiment survey was conducted.
  • Participants were presented with hypothetical foodborne illness outbreak scenarios.
  • Scenarios varied in the number of people reported ill, inclusion of symptoms, FDA investigation status, and a call to action.

Main Results:

  • Individuals are more likely to self-identify as ill when presented with publicized foodborne illness events.
  • The odds of self-reporting increased significantly when the number of affected individuals was higher (8,500) or when symptoms were explicitly mentioned.
  • All tested attributes (number ill, symptoms, FDA investigation, call to action) positively influenced self-reporting.

Conclusions:

  • Publicized foodborne illness events can trigger self-reporting, even without confirmed outbreaks.
  • Factors like the scale of illness and symptom details strongly influence self-reporting behavior.
  • This highlights a potential limitation in using novel data streams for outbreak detection without public health authority confirmation.