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Inferring source attribution from a multiyear multisource data set of Salmonella in Minnesota.

C Ahlstrom1, P Muellner1, S E F Spencer2

  • 1Epi-interactive, Wellington, New Zealand.

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Summary

Chicken was the primary source of Salmonella infections in Minnesota, causing 60% of human cases. This study used a modified Bayesian model to track Salmonella enterica sources and inform public health interventions.

Keywords:
SalmonellaSalmonellosisdata visualizationmolecular epidemiologysource attribution

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Area of Science:

  • Foodborne illness
  • Epidemiology
  • Computational biology

Background:

  • Salmonella enterica is a significant cause of global foodborne illness.
  • Bayesian models are used to attribute human salmonellosis cases to specific sources for intervention prioritization.
  • Evaluating data quality and model logic is crucial for accurate source attribution.

Purpose of the Study:

  • To attribute human salmonellosis cases to specific sources in Minnesota using a modified Bayesian model.
  • To understand pathogen population features, data gaps, and inform policy for Salmonella control.
  • To develop a visual application for exploring large, multiyear datasets.

Main Methods:

  • Analysis of over 12,000 non-typhoidal Salmonella isolates from human and animal sources in Minnesota.
  • Application of a modified Bayesian source attribution model accounting for non-sampled sources.
  • Utilized molecular epidemiological methods and developed a visual attribution application for data exploration.

Main Results:

  • Chicken was attributed as the source for 60% of the 4,672 human Salmonella cases analyzed.
  • A spike in cases attributed to a non-sampled source was observed in the latter half of the study period.
  • High within-source diversity and low between-source similarity were noted, with visual exploration aiding interpretation.

Conclusions:

  • This study provides the first Salmonella source attribution estimates for Minnesota.
  • Findings highlight the importance of chicken as a primary source and identify data gaps for future research.
  • Results will inform public health policies and management strategies to control Salmonella infections in the state.