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Inferring source attribution from a multi-year multi-source dataset of Salmonella in Minnesota
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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
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WRAP-inferring-source-attribution-multi-Salmonella-Minnesota-Spencer-2017.pdf - Accepted Version - Requires a PDF viewer. Download (1503Kb) | Preview |
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Official URL: http://doi.org/10.1111/zph.12351
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 | ||||||||
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| Subjects: | Q Science > QR Microbiology | ||||||||
| Divisions: | Faculty of 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: |
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| 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 | ||||||||
| Funder: | University of Minnesota. Mn Drive |
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