Biology subjects

Fournie, G.

Publications and source records attributed to Fournie, G..

6 recordsLinked to original sources

bioRxiv2025

Prevalence of Campylobacter and non-typhoidal Salmonella along broiler chicken production and distribution networks, Vietnam

Campylobacter and non-typhoidal Salmonella (NTS) are among the most common foodborne pathogens found in chickens at any production stage and cause gastroenteritis in humans. This study aimed to estimate the prevalence of Campylobacter spp. (C. coli and C. jejuni) and NTS in broiler production and distribution networks (PDN) using a Bayesian approach. A cross-sectional study was conducted in four provinces in northern Vietnam between March 2021 and March 2022. A total of 102 sites, including live bird markets, slaughter facilities (slaughterhouses and slaughter points), and their supplying farms, were randomly selected for sampling. Cecal and environmental samples were cultured for isolation of Campylobacter and NTS, with serotypes of NTS determined by targeted analysis of whole genome sequences. Bayesian models were developed to estimate the prevalence of Campylobacter at two levels (bird-level and site-level) and NTS at site-level. The selected best-fitted models indicated that C. jejuni prevalence was primarily influenced by site type, while C. coli was affected by both province and site types. For NTS, only site type was included. The highest overall prevalence of infected broilers was estimated on farms for C. coli (26.2% [95% High Density Interval (HDI): 19.0-36.0%]) and C. jejuni (19.9% [95%HDI 13.0-27.0%]). Slaughter points (97.6% [95%HDI 63.3-99.9%]) and wholesale markets (91.7% [95%HDI 28.2-99.9%]) had the highest probability of C. coli and C. jejuni contamination, respectively, but retail markets had the highest proportion of infected broilers at contaminated sites. NTS contamination was more frequent in markets and slaughter facilities (42.8% [95%HDI 30.8-57.1%]) than on farms (18.6% [95%HDI 9.5-30.1%]). Among 16 detected NTS serotypes, S. Infantis and S. Kentucky were the most common. These findings highlight the widespread contamination of broiler PDNs with Campylobacter and NTS in northern Vietnam, emphasizing the need for enhanced surveillance and control measures in PDNs to mitigate the risk of foodborne transmission. Author SummaryCampylobacter spp. and non-typhoidal Salmonella (NTS) are enteric pathogens that are key global pathogens causing diarrheal diseases. The Campylobacter species that are most frequently responsible for human diseases (campylobacteriosis) are Campylobacter jejuni (C. jejuni) and Campylobacter coli (C. coli). The severity of the salmonellosis depends on host factors and the serotype of NTS. Poultry and poultry products are recognized as major sources of NTS and Campylobacter infections in humans. In Vietnam, these pathogens have been detected in the broiler production and distribution networks (PDN). Factors that may increase the risk of transmission of those pathogens include low biosecurity practices, inadequate waste management, and high farm density. In this work, we estimated the prevalence of Campylobacter spp. (C. coli and C. jejuni) and NTS in PDN markets and slaughterhouses and farms supplying these facilities in four provinces in northern Vietnam. We estimated a high prevalence of Campylobacter contamination in slaughter points and wholesale markets and higher NTS contamination in slaughter facilities and markets than in farms. S. Infantis was one of the most frequently identified serovars. However, it was not reported as being frequently detected in humans in Vietnam. Therefore, serovar may vary depending on the source and region of contamination. Our results help us look at levels of infection/contamination with Campylobacter and NTS in the broiler PDNs, thus informing stakeholders to prevent and control campylobacteriosis and salmonellosis.

microbiology↗

bioRxiv2024

Utilising citizen science data to rapidly assess potential wild bridging hosts and reservoirs of infection: avian influenza outbreaks in Great Britain

High pathogenicity avian influenza virus (HPAIV) is a rapidly evolving orthomyxovirus causing significant economic and environmental harm. Wild birds are a key reservoir of infection and an important source of viral incursions into poultry populations. However, we lack thorough understanding of which wild species drive incursions and whether this changes over time. We explored associations between abundances of 152 avian species and cases of HPAI in poultry premises across Great Britain between October-2021 and January-2023. Spatial generalised additive models were used, with species abundance distributions sourced from eBird modelled predictions. Associations were investigated at the species-specific level and across aggregations of species. During autumn/winter, associations were generally strongest with waterbirds such as ducks and geese; however, we also found significant associations in other groups such as non-native gamebirds, and rapid change in species-specific associations over time. Our results demonstrate the value of citizen science in rapid exploration of wild reservoirs of infection as facilitators of disease incursion into domestic hosts, especially in regions where surveillance programmes in wild birds are absent. This can be a critical step towards improving species-specific biosecurity measures and targeted surveillance; particularly for HPAIV, which has undergone sudden shifts in host-range and continues to rapidly evolve.

ecology↗

bioRxiv2024

Spatial distribution of poultry farms using point pattern modelling: a method to address livestock environmental impacts and disease transmission risks

The distribution of farm locations and sizes is paramount to characterize patterns of disease spread. With some regions undergoing rapid intensification of livestock production, resulting in increased clustering of farms in peri-urban areas, measuring changes in the spatial distribution of farms is crucial to design effective interventions. However, those data are not available in many countries, their generation being resource-intensive. Here, we develop a farm distribution model (FDM), which allows the prediction of locations and sizes of poultry farms in countries with scarce data. The model combines (i) a Log-Gaussian Cox process model to simulate the farm distribution as a spatial Poisson point process, and (ii) a random forest model to simulate farm sizes (i.e. the number of animals per farm). Spatial predictors were used to calibrate the FDM on intensive broiler and layer farm distributions in Bangladesh, Gujarat (Indian state) and Thailand. The FDM yielded realistic farm distributions in terms of spatial clustering, farm locations and sizes, while providing insights on the factors influencing these distributions. Finally, we illustrate the relevance of modelling realistic farm distributions in the context of epidemic spread by simulating pathogen transmission on an array of spatial distributions of farms. We found that farm distributions generated from the FDM yielded spreading patterns consistent with simulations using observed data, while random point patterns underestimated the probability of large outbreaks. Indeed, spatial clustering increases vulnerability to epidemics, highlighting the need to account for it in epidemiological modelling studies. As the FDM maintains a realistic distribution of farm location and sizes, its use to inform mathematical models of disease transmission is particularly relevant for regions where these data are not available.

bioinformatics↗

bioRxiv2023

Amplification of avian influenza viruses along poultry marketing chains in Bangladesh: a controlled field experiment

The prevalence of avian influenza viruses (AIVs) is commonly found to increase dramatically from farms to live bird markets (LBMs). Viral transmission dynamics along marketing chains is, however, poorly understood. To address this gap, we implemented a field experiment altering chicken supply to an LBM in Chattogram, Bangladesh. Chickens traded along altered (intervention) and conventional (control) marketing chains were tested for AIVs. Upon arrival at the LBM, the odds of detecting AIVs did not differ between control and intervention groups. However, 12 hours later, intervention group odds were lower, particularly for broiler chickens, indicating that viral shedding in LBM resulted partly from infections during transport and trade. Curtailing AIV prevalence in LBMs requires mitigating risk in marketing chain nodes preceding chickens delivery at LBMs. Article Summary LineThe high prevalence of avian influenza viruses in marketed chickens cannot be solely attributed to viral transmission within live bird markets but is also influenced by infections occurring prior to the chickens supply to these markets.

ecology↗

bioRxiv2023

EPINEST, an agent-based model to simulate epidemic dynamics in large-scale poultry production and distribution networks

The rapid intensification of poultry production raises important concerns about the associated risks of zoonotic infections. Here, we introduce EPINEST (EPI-demic NEtwork Simulation in poultry Transportation systems): an agent-based modelling designed to simulate pathogen transmission within realistic poultry production and distribution networks. The modular structure of the model allows for easy parameterization to suit specific countries and system configurations. Moreover, the framework enables the replication of a wide range of eco-epidemiological scenarios by incorporating diverse pathogen life-history traits, modes of transmission and interactions between multiple strains and/or pathogens. EPINEST was developed in the context of an interdisciplinary multicentre study conducted in Bangladesh, India, Vietnam and Sri Lanka, and will facilitate the investigation of the spreading patterns of various health hazards such as avian influenza, Campylobacter, Salmonella and antimicrobial resistance in these countries. Furthermore, this modelling framework holds potential for broader application in veterinary epidemiology and One Health research, extending its relevance beyond poultry to encompass other livestock species and disease systems.

bioinformatics↗

bioRxiv2023

Genetic diversity, recombination and cross-species transmission of a waterbird gammacoronavirus in the wild

2.Viruses emerging from wildlife can cause outbreaks in humans and domesticated animals. Predicting the emergence of future pathogens and mitigating their impacts requires an understanding of what shapes virus diversity and dynamics in wildlife reservoirs. In order to better understand coronavirus ecology in wild species, we sampled birds within a coastal freshwater lagoon habitat across five years, focussing on a large population of mute swans (Cygnus olor) and the diverse species that they interact with. We discovered and characterised the full genome of a divergent gammacoronavirus belonging to the Goose coronavirus CB17 species. We investigated the genetic diversity and dynamics of this gammacoronavirus using untargeted metagenomic sequencing of 223 fecal samples from swans of known age and sex, and RT-PCR screening of 1632 additional bird samples. The virus circulated persistently within the bird community; virus prevalence in mute swans exhibited seasonal variations, but did not change with swan age-class or epidemiological year. One whole genome was fully characterised, and revealed that the virus originated from a recombination event involving an undescribed gammacoronavirus species. Multiple lineages of this gammacoronavirus co-circulated within our study population. Viruses from this species have recently been detected in aquatic birds from both the Anatidae and Rallidae families, implying that host species habitat sharing may be important in shaping virus host range. As the host range of the Goose coronavirus CB17 species is not limited to geese, we propose that this species name should be updated to "Waterbird gammacoronavirus 1". Non-invasive sampling of bird coronaviruses may provide a tractable model system for understanding the evolutionary and cross-species dynamics of coronaviruses.

ecology↗