Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Inferring source attribution from a multi-year multi-source dataset of Salmonella in Minnesota

Tools
- Tools
+ Tools

Ahlstrom, Christina, Muellner, Petra, Spencer, Simon E. F., Hong, Samuel, Saupe, Amy, Rovira, Albert, Hedberg, Craig, Perez, Andres, Muellner, Ulrich and Alvarez, Julio (2017) Inferring source attribution from a multi-year multi-source dataset of Salmonella in Minnesota. Zoonoses and Public Health, 64 (8). pp. 589-598. doi:10.1111/zph.12351 ISSN 1863-1959.

[img]
Preview
PDF
WRAP-inferring-source-attribution-multi-Salmonella-Minnesota-Spencer-2017.pdf - Accepted Version - Requires a PDF viewer.

Download (1503Kb) | Preview
[img]
Preview
PDF
WRAP_0974056-st-230217-zph_supportinginfo_20012017.pdf - Supplemental Material - Requires a PDF viewer.

Download (327Kb) | Preview
Official URL: http://doi.org/10.1111/zph.12351

Request Changes to record.

Abstract

Salmonella enterica is a global health concern because of its widespread association with foodborne illness. Bayesian models have been developed to attribute the burden of human salmonellosis to specific sources with the ultimate objective of prioritizing intervention strategies. Important considerations of source attribution models include the evaluation of the quality of input data, assessment of whether attribution results logically reflect the data trends and identification of patterns within the data that might explain the detailed contribution of different sources to the disease burden. Here, more than 12,000 non-typhoidal Salmonella isolates from human, bovine, porcine, chicken and turkey sources that originated in Minnesota were analysed. A modified Bayesian source attribution model (available in a dedicated R package), accounting for non-sampled sources of infection, attributed 4,672 human cases to sources assessed here. Most (60%) cases were attributed to chicken, although there was a spike in cases attributed to a non-sampled source in the second half of the study period. Molecular epidemiological analysis methods were used to supplement risk modelling, and a visual attribution application was developed to facilitate data exploration and comprehension of the large multiyear data set assessed here. A large amount of within-source diversity and low similarity between sources was observed, and visual exploration of data provided clues into variations driving the attribution modelling results. Results from this pillared approach provided first attribution estimates for Salmonella in Minnesota and offer an understanding of current data gaps as well as key pathogen population features, such as serotype frequency, similarity and diversity across the sources. Results here will be used to inform policy and management strategies ultimately intended to prevent and control Salmonella infection in the state.

Item Type: Journal Article
Subjects: Q Science > QR Microbiology
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Salmonella cholerae-suis -- Prevention -- Mathematical models -- Minnesota
Journal or Publication Title: Zoonoses and Public Health
Publisher: Wiley-Blackwell Verlag GmbH
ISSN: 1863-1959
Official Date: December 2017
Dates:
DateEvent
December 2017Published
13 March 2017Available
31 January 2017Accepted
Volume: 64
Number: 8
Page Range: pp. 589-598
DOI: 10.1111/zph.12351
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 24 February 2017
Date of first compliant Open Access: 13 March 2018
Funder: University of Minnesota. Mn Drive

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us