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Vergne, T.

Publications and source records attributed to Vergne, T..

12 recordsLinked to original sources
bioRxiv2026

Territorial risk of high pathogenicity avian influenza in France is associated with farmed species diversity

Animal infectious diseases threaten food security, livestock production and public health. While disease prevention strategies typically target risk factors associated with high-competence hosts or environmental conditions, we hypothesise that lower-competence species can reduce disease risk by diluting effective contact rates between farms. Using spatial statistical models calibrated on three successive French high pathogenicity avian influenza (HPAI) epidemics (2020-2023), we show that greater local diversity of livestock farms, including cattle and pig farms, is associated with significantly lower HPAI risk. Moreover, we show that the positive association between duck houses and HPAI risk weakens as the number of less-competent non-duck poultry houses increases, revealing a dilution effect within poultry production systems. These findings suggest that livestock diversity is a previously overlooked determinant of disease spread and open new research avenues on territorial diversification to complement traditional approaches for the long-term control of high-impact animal infectious diseases.

ecology↗
bioRxiv2026

A modelling challenge to improve preparedness for highly pathogenicavian influenza epidemics: objectives, design and synthetic datageneration

Highly pathogenic avian influenza (HPAI) is among the most significant disease threats to poultry worldwide. Modelling challenges, where independent teams address a common epidemiological scenario, can support the development of response capabilities and strengthen communication between modellers and risk managers. Despite the established benefits of these exercises, they remain uncommon in animal health, and none have previously addressed avian influenza. Here Jolly Island, an artificial landscape of 9160 poultry farms with five production types, was developed and seeded with stochastic HPAI epidemics that ran their course over a three-to-four month period. From January through April 2026, 45 registered teams across five continents investigated a selected synthetic epidemic, producing probabilistic forecasts, inferring transmission drivers, and evaluating counterfactual control policies. Here the objectives, design choices, and data generation of the HPAI Modelling Challenge are described in full, covering transmission processes, surveillance system operations, and control strategy implementation, along with the selected epidemic trajectory, the narrative situation reports supplied to the teams at each phase, and the complete phase-specific datasets released to them. As the opening paper of this special issue, here is provided the foundation upon which all subsequent contributions, where teams document their approaches and analyses, are built.

ecology↗
bioRxiv2026

Integrating multi-host modelling with empirical wildlife-livestock contacts reveals an essential population in a pathogen reservoir

Infections at the animal-human or wildlife-livestock interfaces have severe health and socio-economic consequences. Combined with empirical data, mathematical models can contribute to a better understanding of the reservoirs of these infections, which is a priority for mitigating their impact by using appropriate management interventions. Taking brucellosis in the Bargy massif (French Alps) as an example of a zoonosis at the wildlife-livestock interface, we developed and calibrated a multi-host model integrating data on direct and environment-mediated cross-species contacts from field observations. Estimates of the basic reproduction number (R0) allowed to identify the population of Alpine ibex (Capra ibex) and its environment as an essential host in the reservoir, driving both pathogen maintenance (within-species R0[≥]1: 1.66, 95% credible interval: 1.42-2.03) and its transmission to livestock (between-species R0>0: 0.035, 0.01-0.05). Our approach can be adapted to other multi-host pathogens, which will contribute to improve the understanding and management of these complex systems.

ecology↗
bioRxiv2025

Highly pathogenic avian influenza management in high-density poultry farming areas

The continuous spread of highly pathogenic avian influenza H5 viruses poses significant challenges, particularly in regions with high poultry farm densities where conventional control measures are less effective. Using phylogeographic and phylodynamic tools, we analysed virus spread in Southwestern France in 2020-21, a region with recurrent outbreaks. Following a single introduction, the virus spread regionally, mostly affecting duck farms, peaking in mid-December with a velocity of 27.8 km/week and an effective reproduction number between farms (Re) of 3.8, suggesting the virus can spread beyond current control radii. Transmission declined after late December following preventive culling. Farm infectiousness was estimated around 9 days. Duck farm density was the main driver of virus spread and we identified farm density and proximity thresholds required to maintain effective control (Re < 1). These findings offer actionable guidance to support regional biosecurity and to improve the robustness of the poultry sector to mitigate future outbreaks.

evolutionary biology↗
bioRxiv2024

Global risk mapping of highly pathogenic avian influenza H5N1 and H5Nx in the light of epidemic episodes occurring from 2020 onward

Avian influenza (AI) is a highly contagious viral disease affecting poultry and wild water birds, posing significant global challenges due to its high mortality rates and economic impacts. Highly pathogenic avian influenza (HPAI) outbreaks, particularly those caused by H5N1 and its variants, have surged since their first occurrence in 1959. The HPAI H5N1 clade 2.3.4.4b viruses have notably expanded their geographical reach, affecting numerous countries, diverse avian species, and now wild and domestic mammals. Using an ecological niche modelling approach, this study aims to elucidate the environmental factors associated with the increased HPAI H5 cases since 2020, investigate potential shifts in ecological niches, and predict new areas suitable for local viral circulation. Focusing on H5N1 and H5Nx strains, we have developed ecological niche models for HPAI cases in both wild and domestic birds while considering two distinct periods: 2015-2020 and 2020-2022. Key environmental predictors include chicken and duck population density, human density, distance to water bodies, and several land cover variables. Post-2020, we observe a notable increase in the relative influence of some of these predictors, such as intensive chicken population density and cultivated vegetation. The resulting risk maps reveal notable ecological suitability for local HPAI H5 circulation in Europe, Asia, as well as North and South America, with notable expansions of the areas at risk post-2020. The spatial distribution of HPAI H5 occurrences in wild birds appears to be primarily correlated with urban areas and open water regions. Overall, global risk maps derived from our models identify regions at risk where surveillance and control measures should be prioritised. Finally, our analyses also highlight a potential shift in the diversity of wild bird species affected by HPAI outbreaks, with a higher variety of avian species, particularly sea birds, being impacted post-2020. This increased diversity could suggest that ecological shifts in HPAI H5 circulation may be accompanied by a broader range of susceptible species. Overall, these results further contribute to the understanding of HPAI epidemiology.

ecology↗
bioRxiv2024

A multi-host mechanistic model of African swine fever emergence and control in Romania

African swine fever (ASF) has devastating effects on swine production, farmer livelihood, animal welfare, and biodiversity. Extremely difficult to control, epidemic management is further complicated when spillover between domestic pig and wild boar populations is suspected. To quantify ASF viral transmission between domestic pigs and wild boar, a spatially-explicit stochastic mechanistic model was constructed using village centroids to represent aggregated backyard pig herds and a hexagonal raster of forest coverage to represent wild boar abundance. The model was parameterized to the initial six months of the ongoing Romanian epidemic through approximate Bayesian computation. It was estimated that a median of 69.4% (interquartile range: 53.0-80.0%) of domestic pig herd cases came from other infected domestic pig herds while 20.4% (11.2-33.8%) originated from infected wild boar sources, and 8.4% (4.7-14.2%) stemmed from external sources not explicitly represented. Also, 31.9% of infected wild boar habitat (16.7-56.2%) originated from domestic pig herds and 68.1% (43.8-83.3%) came from neighboring infected wild boar populations. Furthermore, it was found that habitats with a forest coverage greater than 15% were 2.6 times more infectious and 5.3 times more susceptible than other habitats. All alternative control scenarios, including culling domestic pig herds upon local domestic pig or wild boar case detection, improved epidemic outcomes, with the greatest decrease in final epidemic size being observed from the reactive culling of entire villages following case detection. These results can be used to further inform policy recommendations in ASF-epidemic regions. Significance StatementThe current African swine fever (ASF) pandemic is devastating to affected nations, and quantifying transmission parameters is critical to informing control strategies. Disease spillover between wild and domestic hosts further complicates control efforts, yet the influence of spillover events on epidemic propagation remains unknown. Using the context of Romania--one of the European nations with the most severe epidemic and where spillover transmission is strongly suspected--we show that targeting spillover mechanisms is critical for achieving holistic disease control, and then demonstrate the impact of alternative control scenarios had they been enacted. These results can inform control strategy policy decisions in the many nations at-risk for or actively experiencing ASF epidemics.

ecology↗
bioRxiv2022

Two major epidemics of highly pathogenic avian influenza virus H5N8 and H5N1 in domestic poultry in France, 2020-2022

The spread of highly pathogenic avian influenza (HPAI) viruses worldwide has serious consequences for animal health and a major economic impact on the poultry production sector. Since 2014, Europe has been severely hit by several HPAI epidemics, with France being the most affected country. Most recently, France was again affected by two devastating highly pathogenic avian influenza epidemics in 2020-21 and 2021-22. We conducted a descriptive analysis of the 2020-21 and 2021-22 epidemics, in a first step towards identifying the poultry sectors remaining vulnerabilities regarding HPAI viruses in France. We examined the spatio-temporal distribution of outbreaks that occurred in France in 2020-21 and 2021-22, and we assessed the outbreaks spatial distribution in relation to two High-Risk Zones recently incorporated into French legislation to strengthen HPAI prevention and control. There were 468 reported outbreaks during the 2020-21 epidemic, and 1,223 outbreaks during the 2021-22 epidemic. In both epidemics, most outbreaks (80.6% and 74.0%) were located into the two High-Risk Zones. The southwestern High-Risk Zone was affected in both epidemics, while the western High-Risk Zone was affected for the first time in 2021-22, explaining the extremely high number of outbreaks reported. We showed that the spatial distribution model used to create the two High-Risk Zones was able to predict the location of outbreaks for the 2020-21 and 2021-22 epidemics. These zones were characterized by high poultry farm densities; future efforts should therefore focus on reducing the density of susceptible poultry in highly dense areas.

ecology↗
bioRxiv2021

The African swine fever modelling challenge: objectives, model description and synthetic data generation

African swine fever (ASF) is an emerging disease currently spreading at the interface between wild boar and pig farms in Europe and Asia. Current disease control regulations, which involve massive culling with significant economic and animal welfare costs, need to be improved. Modelling enables relevant control measures to be explored, but conducting the exercise during an epidemic is extremely difficult. Modelling challenges enhance modellers ability to timely advice policy makers, improve their readiness when facing emerging threats, and promote international collaborations. The ASF-Challenge, which ran between August 2020 and January 2021, was the first modelling challenge in animal health. In this paper, we describe the objectives and rules of the challenge. We then demonstrate the mechanistic multi-host model that was used to mimic as accurately as possible an ASF-like epidemic, provide a detailed explanation of the surveillance and intervention strategies that generated the synthetic data, and describe the different management strategies that were assessed by the competing modelling teams. We then outline the different technical steps of the challenge as well as its environment. Finally, we synthesize the lessons we learnt along the way to guide future modelling challenges in animal health. HighlightsO_LIThe 1st modelling challenge in animal health mimics ASF spread with synthetic data C_LIO_LIA mechanistic spatially-explicit stochastic model was developed to represent ASF spread and control C_LIO_LISynthetic data concerned date and location of detected cases in pigs and wild boar C_LIO_LIModelling ASF at the wildlife-livestock interface illustrates the reservoir role of wild fauna C_LI

ecology↗
bioRxiv2021

Phylodynamic assessment of control measures for highly pathogenic avian influenza epidemics in France

Phylodynamic methods have successfully been used to describe viral spread history but their applications for assessing specific control measures are rare. In 2016-17, France experienced a devastating epidemic of a highly pathogenic avian influenza virus (H5N8 clade 2.3.4.4b). Using 196 viral genomes, we conducted a phylodynamic analysis combined with generalised linear model and showed that the large-scale preventive culling of ducks significantly reduced the viral spread between departements (French administrative division). We also found that the virus likely spread more frequently between departements that shared borders, but the spread was not linked to duck transport between departements. Duck transport within departements increased the within-departement transmission intensity, although the association was weak. Together, these results indicated that the virus spread in short-distances, either between adjacent departements or within departements. Results also suggested that the restrictions on duck transport within departements might not have stopped the viral spread completely. Overall, by testing specific hypothesis related to different control measures, we demonstrated that phylodynamics methods are capable of investigating the impacts of control measures on viral spread.

evolutionary biology↗
bioRxiv2021

Modelling the deathbed of ASF-infected wild boars in South Korea using 2019-2020 national surveillance data

In September 2019, African swine fever (ASF) was reported in South Korea for the first time. Since then, more than 651 ASF cases in wild boars and 14 farm outbreaks have been notified in the country. The purpose of this study was to characterize the spatial distribution of ASF-positive wild boar carcasses to identify the risk factors associated with the presence of ASF and number of ASF-positive wild boar carcasses in the affected areas. To achieve this objective, we divided the study into two periods (October 2, 2019, to January 19, 2020, and January 19 to April 28, 2020) and aggregated the number of reported ASF-positive carcasses into a regular grid of hexagons. To account for imperfect detection, we adjusted spatial zero-inflated Poisson regression models to the number of ASF-positive wild boar carcasses per hexagons. During the first study period, only proximity to North Korea was identified as a risk factor for the presence of African swine fever virus (ASFV). In addition, there were more reports in the affected hexagons with a high habitat suitability for wild boar, low heat load index (HLI), and high human density. During the second study period, proximity to an ASF-positive carcass reported during the first period was the only significant risk factor for the presence of ASF-positive carcasses. Additionally, high HLI and low elevation were associated with an increased number of ASF-positive carcasses reported in the affected hexagons. Although the proportion of ASF-affected hexagons increased from 0.06 (95% credible interval [CrI]: 0.05-0.07) to 0.09 (95% CrI: 0.08-0.10), the probability of reporting ASF-affected hexagons increased substantially from 0.49 (95% CrI: 0.41-0.57) to 0.73 (95% CrI: 0.66-0.81) between the two study periods. These results can be used to further advance risk-based surveillance.

ecology↗
bioRxiv2020

Inferring within-flock transmission dynamics of highly pathogenic avian influenza (H5N8) in France, 2020

Following the emergence of highly pathogenic avian influenza (H5N8) in France in early December 2020, we used duck mortality data of the index case to investigate within-flock transmission dynamics. A stochastic epidemic model was adjusted to the daily mortality data and model parameters were estimated using an approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithm. Results suggested that the virus was introduced 4 days (95% credible interval: 3 - 5) prior to the day suspicion was reported and that the transmission rate was 3.7 day-1 (95%CI: 2.6 - 5.3). On average, ducks started being infectious 3.1 hours (95%CI: 0.4 - 8.0) after infection and remained infectious for 4.4 days (95%CI: 3.1 - 5.6). Model outputs also suggested that the number of infectious ducks was already 3239 (95%CI: 26 - 3706) the day before suspicion, emphasising the substantial latent threat this virus could pose to other poultry farms and to neighbouring wildlife. These estimations can be applied to upcoming outbreaks and made available to veterinary services within few hours. This study illustrates how mechanistic models can provide rapid relevant insights to contribute to the management of infectious disease outbreaks of farmed animals.

ecology↗