ArticleJournal of the Royal Society, Interface2019
Extending statistical models for source attribution of zoonotic diseases: a study of campylobacteriosis.
Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- New Campylobacter Lineages in New Zealand Freshwater: Pathogenesis and Public Health Implications.Environmental microbiology · 2024Article
- Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world.Nature reviews. Microbiology · 2024Review
- A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.PLoS computational biology · 2024Article
- Source attribution of campylobacteriosis in Australia, 2017-2019.Risk analysis : an official publication of the Society for Risk Analysis · 2023Article
- Source attribution of salmonellosis by time and geography in New South Wales, Australia.BMC infectious diseases · 2022Article
- Phylogenetic Analysis Reveals Source Attribution Patterns forMicroorganisms · 2021Article
- AB_SA: Accessory genes-Based Source Attribution - tracing the source ofMicrobial genomics · 2020Article
- Campylobacteriosis associated with the consumption of unpasteurised milk: findings from a sentinel surveillance site.Epidemiology and infection · 2020Observational
- Extending statistical models for source attribution of zoonotic diseases: a study of campylobacteriosis.Journal of the Royal Society, Interface · 2019Article
- Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases.Frontiers in microbiology · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preventing and controlling zoonoses through the design and implementation of public health policies requires a thorough understanding of transmission pathways. Modelling jointly the epidemiological data and genetic information of microbial isolates derived from cases provides a methodology for tracing back the source of infection. In this paper, the attribution probability for human cases of campylobacteriosis for each source, conditional on the extent to which each case resides in a rural compared to urban environment, is estimated. A model that incorporates genetic data and evolutionary processes is applied alongside a newly developed genetic-free model. We show that inference from each model is comparable except for rare microbial genotypes. Further, the effect of 'rurality' may be modelled linearly on the logit scale, with increasing rurality leading to the increasing likelihood of ruminant-sourced campylobacteriosis.
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Registered trials
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