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sourceR: Classification and source attribution of infectious agents among heterogeneous populations.

Abstract Zoonotic diseases are a major cause of morbidity, and productivity losses in both human and animal populations. Identifying the source of food-borne zoonoses (e.g. an animal reservoir or food product) is crucial for the identification and prioritisation of food safety interventions. For many zoonotic diseases it is difficult to attribute human cases to sources of infection because there is little epidemiological information on the cases. However, microbial strain typing allows zoonotic pathogens to be categorised, and the relative frequencies of the strain types among the sources and in human cases allows inference on the likely source of each infection. We introduce sourceR, an R package for quantitative source attribution, aimed at food-borne diseases. It implements a Bayesian model using strain-typed surveillance data from both human cases and source samples, capable of identifying important sources of infection. The model measures the force of infection from each source, allowing for varying survivability, pathogenicity and virulence of pathogen strains, and varying abilities of the sources to act as vehicles of infection. A Bayesian non-parametric (Dirichlet process) approach is used to cluster pathogen strain types by epidemiological behaviour, avoiding model overfitting and allowing detection of strain types associated with potentially high "virulence". sourceR is demonstrated using Campylobacter jejuni isolate data collected in New Zealand between 2005 and 2008. Chicken from a particular poultry supplier was identified as the major source of campylobacteriosis, which is qualitatively similar to results of previous studies using the same dataset. Additionally, the software identifies a cluster of 9 multilocus sequence types with abnormally high 'virulence' in humans. sourceR enables straightforward attribution of cases of zoonotic infection to putative sources of infection. As sourceR develops, we intend it to become an important and flexible resource for food-borne disease attribution studies.
PMID
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Authors

Mayor MeshTerms

Bacteria

Foodborne Diseases

Models, Biological

Software

Zoonoses

Keywords
Journal Title plos computational biology
Publication Year Start




PMID- 28558033
OWN - NLM
STAT- MEDLINE
DA  - 20170530
DCOM- 20170626
LR  - 20170626
IS  - 1553-7358 (Electronic)
IS  - 1553-734X (Linking)
VI  - 13
IP  - 5
DP  - 2017 May
TI  - sourceR: Classification and source attribution of infectious agents among
      heterogeneous populations.
PG  - e1005564
LID - 10.1371/journal.pcbi.1005564 [doi]
AB  - Zoonotic diseases are a major cause of morbidity, and productivity losses in both
      human and animal populations. Identifying the source of food-borne zoonoses (e.g.
      an animal reservoir or food product) is crucial for the identification and
      prioritisation of food safety interventions. For many zoonotic diseases it is
      difficult to attribute human cases to sources of infection because there is
      little epidemiological information on the cases. However, microbial strain typing
      allows zoonotic pathogens to be categorised, and the relative frequencies of the 
      strain types among the sources and in human cases allows inference on the likely 
      source of each infection. We introduce sourceR, an R package for quantitative
      source attribution, aimed at food-borne diseases. It implements a Bayesian model 
      using strain-typed surveillance data from both human cases and source samples,
      capable of identifying important sources of infection. The model measures the
      force of infection from each source, allowing for varying survivability,
      pathogenicity and virulence of pathogen strains, and varying abilities of the
      sources to act as vehicles of infection. A Bayesian non-parametric (Dirichlet
      process) approach is used to cluster pathogen strain types by epidemiological
      behaviour, avoiding model overfitting and allowing detection of strain types
      associated with potentially high "virulence". sourceR is demonstrated using
      Campylobacter jejuni isolate data collected in New Zealand between 2005 and 2008.
      Chicken from a particular poultry supplier was identified as the major source of 
      campylobacteriosis, which is qualitatively similar to results of previous studies
      using the same dataset. Additionally, the software identifies a cluster of 9
      multilocus sequence types with abnormally high 'virulence' in humans. sourceR
      enables straightforward attribution of cases of zoonotic infection to putative
      sources of infection. As sourceR develops, we intend it to become an important
      and flexible resource for food-borne disease attribution studies.
FAU - Miller, Poppy
AU  - Miller P
AUID- ORCID: http://orcid.org/0000-0002-7644-6549
AD  - CHICAS, Faculty of Health and Medicine, Lancaster University, Lancaster, England,
      United Kingdom.
FAU - Marshall, Jonathan
AU  - Marshall J
AUID- ORCID: http://orcid.org/0000-0003-0758-9658
AD  - Institute of Fundamental Sciences, Massey University, Palmerston North, New
      Zealand.
AD  - mEpiLab, Massey University, Palmerston North, New Zealand.
FAU - French, Nigel
AU  - French N
AD  - mEpiLab, Massey University, Palmerston North, New Zealand.
AD  - New Zealand Food Safety Science and Research Centre, Palmerston North, New
      Zealand.
AD  - New Zealand Institute for Advanced Studies, Auckland, New Zealand.
FAU - Jewell, Chris
AU  - Jewell C
AUID- ORCID: http://orcid.org/0000-0002-7902-2178
AD  - CHICAS, Faculty of Health and Medicine, Lancaster University, Lancaster, England,
      United Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20170530
PL  - United States
TA  - PLoS Comput Biol
JT  - PLoS computational biology
JID - 101238922
SB  - IM
MH  - Animals
MH  - *Bacteria/classification/pathogenicity
MH  - Bayes Theorem
MH  - Campylobacter Infections/epidemiology/microbiology
MH  - Campylobacter jejuni/classification/pathogenicity
MH  - Computational Biology/methods
MH  - *Foodborne Diseases/epidemiology/microbiology
MH  - Humans
MH  - *Models, Biological
MH  - New Zealand
MH  - *Software
MH  - *Zoonoses/epidemiology/microbiology
PMC - PMC5473572
EDAT- 2017/05/31 06:00
MHDA- 2017/06/27 06:00
CRDT- 2017/05/31 06:00
PHST- 2017/01/30 [received]
PHST- 2017/05/10 [accepted]
PHST- 2017/06/16 [revised]
AID - 10.1371/journal.pcbi.1005564 [doi]
AID - PCOMPBIOL-D-17-00170 [pii]
PST - epublish
SO  - PLoS Comput Biol. 2017 May 30;13(5):e1005564. doi: 10.1371/journal.pcbi.1005564. 
      eCollection 2017 May.