Dataset Information

Tracing the source of campylobacteriosis.

ABSTRACT: Campylobacter jejuni is the leading cause of bacterial gastro-enteritis in the developed world. It is thought to infect 2-3 million people a year in the US alone, at a cost to the economy in excess of US $4 billion. C. jejuni is a widespread zoonotic pathogen that is carried by animals farmed for meat and poultry. A connection with contaminated food is recognized, but C. jejuni is also commonly found in wild animals and water sources. Phylogenetic studies have suggested that genotypes pathogenic to humans bear greatest resemblance to non-livestock isolates. Moreover, seasonal variation in campylobacteriosis bears the hallmarks of water-borne disease, and certain outbreaks have been attributed to contamination of drinking water. As a result, the relative importance of these reservoirs to human disease is controversial. We use multilocus sequence typing to genotype 1,231 cases of C. jejuni isolated from patients in Lancashire, England. By modeling the DNA sequence evolution and zoonotic transmission of C. jejuni between host species and the environment, we assign human cases probabilistically to source populations. Our novel population genetics approach reveals that the vast majority (97%) of sporadic disease can be attributed to animals farmed for meat and poultry. Chicken and cattle are the principal sources of C. jejuni pathogenic to humans, whereas wild animal and environmental sources are responsible for just 3% of disease. Our results imply that the primary transmission route is through the food chain, and suggest that incidence could be dramatically reduced by enhanced on-farm biosecurity or preventing food-borne transmission.

SUBMITTER: Wilson DJ

PROVIDER: S-EPMC2538567 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

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Tracing the source of campylobacteriosis.

Wilson Daniel J DJ Gabriel Edith E Leatherbarrow Andrew J H AJ Cheesbrough John J Gee Steven S Bolton Eric E Fox Andrew A Fearnhead Paul P Hart C Anthony CA Diggle Peter J PJ

PLoS genetics 20080926 9

Campylobacter jejuni is the leading cause of bacterial gastro-enteritis in the developed world. It is thought to infect 2-3 million people a year in the US alone, at a cost to the economy in excess of US $4 billion. C. jejuni is a widespread zoonotic pathogen that is carried by animals farmed for meat and poultry. A connection with contaminated food is recognized, but C. jejuni is also commonly found in wild animals and water sources. Phylogenetic studies have suggested that genotypes pathogenic ...[more]

PMID: 18818764

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Project description:Campylobacter spp. are a leading and increasing cause of gastrointestinal infections worldwide. Source attribution, which apportions human infection cases to different animal species and food reservoirs, has been instrumental in control- and evidence-based intervention efforts. The rapid increase in whole-genome sequencing data provides an opportunity for higher-resolution source attribution models. Important challenges, including the high dimension and complex structure of WGS data, have inspired concerted research efforts to develop new models. We propose network analysis models as an accurate, high-resolution source attribution approach for the sources of human campylobacteriosis. A weighted network analysis approach was used in this study for source attribution comparing different WGS data inputs. The compared model inputs consisted of cgMLST and wgMLST distance matrices from 717 human and 717 animal isolates from cattle, chickens, dogs, ducks, pigs and turkeys. SNP distance matrices from 720 human and 720 animal isolates were also used. The data were collected from 2015 to 2017 in Denmark, with the animal sources consisting of domestic and imports from 7 European countries. Clusters consisted of network nodes representing respective genomes and links representing distances between genomes. Based on the results, animal sources were the main driving factor for cluster formation, followed by type of species and sampling year. The coherence source clustering (CSC) values based on animal sources were 78%, 81% and 78% for cgMLST, wgMLST and SNP, respectively. The CSC values based on Campylobacter species were 78%, 79% and 69% for cgMLST, wgMLST and SNP, respectively. Including human isolates in the network resulted in 88%, 77% and 88% of the total human isolates being clustered with the different animal sources for cgMLST, wgMLST and SNP, respectively. Between 12% and 23% of human isolates were not attributed to any animal source. Most of the human genomes were attributed to chickens from Denmark, with an average attribution percentage of 52.8%, 52.2% and 51.2% for cgMLST, wgMLST and SNP distance matrices respectively, while ducks from Denmark showed the least attribution of 0% for all three distance matrices. The best-performing model was the one using wgMLST distance matrix as input data, which had a CSC value of 81%. Results from our study show that the weighted network-based approach for source attribution is reliable and can be used as an alternative method for source attribution considering the high performance of the model. The model is also robust across the different Campylobacter species, animal sources and WGS data types used as input.

Project description:Digital contact tracing presents numerous advantages compared to manual contact tracing methods, especially in terms of enhanced speed and automation. Nevertheless, a lack of comprehensive evaluation regarding functionality, efficiency, benefits, and acceptance within communities remains. Here we primarily focus on the functionality of THEA-GS, an open-source digital contact tracing tool developed through consultation with stakeholders. Additionally, we provide insights from its implementation on a limited sample of haulage drivers in Uganda, serving as a representative case for a low- and middle-income country. THEA-GS comprises two primary components: (a) a smartphone application, and (b) a suite of server-programs responsible for data processing and analysis, including databases and a web-based interface featuring dashboards. In essence, the mobile application records the timestamped location of haulage drivers within the road network and identifies possible transmission hotspots by analyzing factors such as the duration of stops and the communities associated with them. The tool can be integrated with national infrastructure to compare drivers' diagnostic results and contact structure, thereby generating individual and community risk assessments relative to the road network. During the Omicron-variant wave of the COVID-19 pandemic, a total of 3,270 haulage drivers were enrolled between October 2021 and October 2022. Around 75% of these drivers utilized THEA-GS for approximately two months. Based on an analysis of 3,800 test results, which included 48 positive cases, 125 contacts, and 40 million time-stamped GPS points, THEA-GS shows a significant speed improvement, being approximately 90 times faster than MCT. For instance, the average time from sample collection to notifying a case and their contacts was approximately 70 and 80 min, respectively. The adoption of this tool encountered challenges, mainly due to drivers' awareness of its purpose and benefits for public health. THEA-GS is a place-based digital contact tracing tool specifically designed to assist National Public Health Institutions in managing infectious disease outbreaks involving the haulage industry as a high-risk group. While its utility, acceptance, and accuracy have not been fully evaluated, our preliminary tests conducted in Uganda indicate the tool's functionality is robust, but social acceptance and adoption are heavily reliant on establishing trust among users.

Dataset Information

Tracing the source of campylobacteriosis.

Publications

Tracing the source of campylobacteriosis.

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