title: Inferring source attribution from a multiyear multisource data set of Salmonella in Minnesota.
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.
authors.affiliation: Epi-interactive, Wellington, New Zealand.
authors.forename: C
authors.identifier: 0000-0001-5414-8076
authors.initials: C
authors.lastname: Ahlstrom
authors.affiliation: Epi-interactive, Wellington, New Zealand.
authors.forename: P
authors.initials: P
authors.lastname: Muellner
authors.affiliation: University of Warwick, Coventry, UK.
authors.forename: S E F
authors.initials: SEF
authors.lastname: Spencer
authors.affiliation: Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, USA.
authors.forename: S
authors.initials: S
authors.lastname: Hong
authors.affiliation: Minnesota Department of Health, Saint Paul, MN, USA.
authors.forename: A
authors.initials: A
authors.lastname: Saupe
authors.affiliation: Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, USA.
authors.forename: A
authors.initials: A
authors.lastname: Rovira
authors.affiliation: Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
authors.forename: C
authors.initials: C
authors.lastname: Hedberg
authors.affiliation: Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, USA.
authors.forename: A
authors.initials: A
authors.lastname: Perez
authors.affiliation: Epi-interactive, Wellington, New Zealand.
authors.forename: U
authors.initials: U
authors.lastname: Muellner
authors.affiliation: Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, USA.
authors.forename: J
authors.identifier: 0000-0002-8999-9417
authors.initials: J
authors.lastname: Alvarez
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002645:Chickens / Q000382:microbiology; D062665:Epidemiological Monitoring; D005516:Food Microbiology; D006801:Humans; D008460:Meat / Q000382:microbiology; D008910:Minnesota / Q000453:epidemiology; D008954:Models, Biological; D012475:Salmonella / Q000235:genetics*; D012478:Salmonella Food Poisoning / Q000453:epidemiology*; D012481:Salmonella Infections, Animal / Q000453:epidemiology / Q000382:microbiology; D013997:Time Factors; D015047:Zoonoses
keywords: 
  Salmonella
  ; Salmonellosis; data visualization; molecular epidemiology; source attribution