# Poultry farm density and proximity drive highly pathogenic avian influenza spread

Claire Guinat, Cecilia Valenzuela Agüí, François-Xavier Briand, Debapriyo Chakraborty, Lisa Fourtune, Sébastien Lambert, Andrea Jimenez Pellicer, Severine Ratureau, Guillaume Gerbier, Louis Du Plessis, et al.

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## To cite this version:

Claire Guinat, Cecilia Valenzuela Agüí, François-Xavier Briand, Debapriyo Chakraborty, Lisa Fourtune, et al. Poultry farm density and proximity drive highly pathogenic avian influenza spread. Communications Biology, 2025, 8 (1), pp.1306. 10.1038/s42003-025-08687-4. hal-05244266

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# Poultry farm density and proximity drive highly pathogenic avian influenza spread

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Claire Guinat Ⓔ<fnref n="1" />, Cecilia Valenzuela Agúi Ⓔ²,³,⁷, François-Xavier Briand Ⓔ⁴,⁷,

Debapriyo Chakraborty Ⓔ<fnref n="1" />,⁵, Lisa Fournune Ⓔ<fnref n="1" />, Sébastien Lambert Ⓔ<fnref n="1" />, Andrea Jimenez Pellicer⁶,

Severine Rautureau⁶, Guillaume Gerbier⁶, Louis du Plessis Ⓔ²,³, Tanja Stadler Ⓔ²,³, Beatrice Grasland Ⓔ⁴,

Mathilde C. Paul Ⓔ<fnref n="1" /> & Timothée Vergne Ⓔ<fnref n="1" />

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 and 2021, a region with recurrent outbreaks. Following a single introduction, the virus spread regionally, mostly affecting duck farms, with an average velocity of 10.5 km/week and a peak at 27.8 km/week in mid-December. The effective reproduction number between farms ($R_e$) exceeded 1 in December, peaking at 3.8 in mid-December. Transmission declined after late December, coinciding with extensive preventive culling, suggesting that standard 3- and 10-km zones were more effective when combined with timely, large-scale interventions. Farm infectiousness was estimated around 9 days, highlighting a critical window of undetected viral shedding and transmission risk. Duck farm density and poultry farm proximity were key drivers of virus spread, likely due to the higher virus susceptibility and transmission efficiency of ducks compared to chickens. We identified density and proximity thresholds required to maintain effective control ($R_e < 1$). These findings offer actionable guidance to support regional biosecurity and to improve the robustness of the poultry sector to mitigate future outbreaks.

In recent years, the unprecedented and widespread outbreaks of highly pathogenic avian influenza (HPAI) H5 viruses, particularly clade 2.3.4.4b, have resulted in catastrophic socioeconomic and ecological impacts. Initially emerging in Asia, these viruses have been carried across continents by migratory wild birds, leading to devastating poultry outbreaks since 2016¹. Alarmingly, since 2021, these viruses have shown endemic circulation in wild bird populations and recently expanded their host range to affect wild and domestic mammals not typically susceptible²,³. Moreover, sporadic human cases have been confirmed in multiple countries across four continents from 2020 to 2024¹. The virus has been detected in a wide range of host and geographic, heightening concerns about its potential to adapt to mammals, fuelling fears of an HPAI pandemic in humans². Consequently, managing HPAI outbreaks effectively has now become a critical global health objective⁶.

France has been one of the most heavily affected countries in Europe by recurrent waves of HPAI since 2016⁷⁻⁹. In response to these waves, France has progressively implemented a series of regulations aimed at preventing the introduction and spread of the virus. These included preventive culling, poultry movement restrictions, mandatory biosecurity training for poultry producers and biosecurity audits. In October 2021, the establishment of a high-risk diffusion zone (ZRD) refined the area of priority where measures had to be reinforced during high-risk periods, such as duck flock pre-movement testing and compulsory indoor housing. Despite these efforts, outbreaks have continued to occur in regions with very high poultry farm densities, such as southwestern and western France, where the close proximity of farms likely makes these measures less effective and may also increase the likelihood of virus introductions from wild birds, possibly due to increased resource availability. In October 2023, France initiated a vaccination campaign, showing promising results with fewer outbreaks

<footnote n="1">¹Interactions Hôtes-Agents Pathogènes (IHAP), Université de Toulouse, INRAE, ENVT, Toulouse, France. ²Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland. ³Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland. ⁴French Agency for Food, Environmental and Occupational Health & Safety (ANSES) Laboratory of Ploufragan-Plouzané-Niort, Ploufragan, France. ⁵Geomatys SAS, 1000 Avenue Agropolis, Montpellier, France. ⁶Direction Générale de l'Alimentation (DGAL), Ministère de l'Agriculture et la Souveraineté Alimentaire, Paris, France. ⁷These authors contributed equally: Cecilia Valenzuela Agúi, François-Xavier Briand. ✉e-mail: claire.guinat@envt.fr</footnote>
compared to previous years¹⁰. However, relying solely on vaccination may not be sustainable in the long term due to high costs, logistical challenges, mandatory surveillance protocols and the risk of trade restrictions¹¹. These challenges underscore the need to rethink the spatial distribution and organisation of poultry farms, particularly in these high-density areas where tailored strategies are essential for effectively containing the virus and preventing its spread between farms¹².

Evaluating strategies for effectively stopping the spread of HPAI from one farm to another requires accurately reconstructing the epidemiological links between farms and identifying associated risk factors. This can be achieved by analysing viral genetic sequences within a phylodynamic framework, which allows us to infer where and when the virus spread. By integrating generalised linear models (GLMs) into phylodynamic analyses, we can further identify the key drivers of virus spread¹³,¹⁴. Using this joint approach, recent studies have identified significant associations of a range of factors, such as geographical proximity¹⁵⁻¹⁷, poultry trade¹⁸⁻²⁰ and poultry population density²¹, with virus spread in poultry. However, these studies were often limited by the number of available genetic sequences and the lack of precise location data, typically identified only at the country or regional level. As a result, past analyses were typically restricted to broad scales, making it difficult to draw actionable recommendations or develop effective control measures that account for local variations in farming practices or ecological conditions.

In this study, we build on these approaches to identify the key factors driving the spread of HPAI H5N8 viruses (clade 2.3.4.4b) between farms in southwestern France in 2020 and 2021 and provide options to enhance HPAI management in this high poultry farm density area. We applied a suite of phylogeographic and phylodynamic models to a comprehensive dataset of viral genetic sequences. Our phylodynamic analysis was extended with a GLM to account for a set of detailed variables being potential

predictors of between-farm transmission such as poultry farm production and environmental data. Furthermore, our study leverages another dataset that distinguishes between operational and empty poultry farms during the study period. Our findings highlight the critical role that farm density and proximity on HPAI spread and derive practical recommendations to improve the robustness of poultry production sector against HPAI.

# Results

## Identifying the number of HPAI introductions to poultry farms

We analysed 432 genetic sequences of HPAI H5N8 viruses (clade 2.3.4.4b) from 475 confirmed infected poultry farms in southwestern France between December 2, 2020 and March 20, 2021. After subsampling to remove identical sequences with the same sampling date, species and location, our final dataset comprised 381 sequences, representing 80.2% of confirmed infected poultry farms (Fig. 1A). As for the outbreaks, most sequences originated from infected duck farms, with a peak in early January 2021 (Fig. 1A) and were from six departments in southwestern France (Fig. 1B). When combined with background sequences from Europe, the French dataset formed a monophyletic clade (branch support = 78.2%) (Fig. 1C).

## Overview of spatio-temporal dispersal of HPAI

We investigated the spatio-temporal spread of HPAI between farms based on genetic sequences from the French dataset using a Bayesian continuous phylogeographic analysis. Overall, the virus was maintained in duck flocks in southwestern France with sporadic spillover events to other poultry species, in particular chicken flocks (Fig. 2A). The virus initially emerged in Landes department, where it persisted from December 2020 to mid-February 2021, with subsequent transmission events to the southern Hautes-Pyrénées and Pyrénées-Atlantiques departments (Fig. 2B). By mid-February, the outbreaks were largely under control, although a few local

<figure><img src="image_5.png" /><figcaption>**Fig. 1** | Distribution and phylogeny of HPAI H5N8 clade 2.3.4.4b genetic sequences from outbreaks in southwestern France, 2020–21. The left and middle panel show the temporal (A) and spatial (B) distributions of HPAI H5N8 clade 2.3.4.4b outbreaks (n = 464) and genetic sequences (n = 381) in France in 2020 and</figcaption></figure>

2021. The right panel C shows the maximum likelihood phylogeny of the French genetic sequences (n = 381) along with background sequences from Europe from September 7, 2020 to May 1, 2021 (n = 342).
A

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B

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C

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D

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**Fig. 2 | Transmission dynamics of HPAI H5N8 clade 2.3.4.4b between farms in southwestern France, 2020–21.** The left panel A shows the maximum clade credibility (MCC) tree of HPAI H5N8 virus sequenced from poultry farms during the 2020 and 2021 wave in France. The tip colours represent the host species present in the infected farms. Grey bars represent the 95% highest posterior density (HPD) intervals reflecting uncertainty about internal node dates. The middle panel B represents the same MCC tree on a spatial map. The top right panel C shows the

weighted lineage dispersal velocity over time with bold line representing the median values and the filled area represents the 95% highest posterior density intervals. The bottom right panel D shows the weekly estimates of the effective reproduction number ($R_e$) over time inferred with a phylodynamic and epidemiological models. The bold line represents the median values and the filled area represents the 95% highest posterior density intervals and the 95% equal-tailed credible intervals. The horizontal black dashed line indicates a $R_e$ value of 1.

transmission events continued within the Gers department. We also estimated how fast HPAI spread between farms by inferring the weighted lineage dispersal velocity, which corresponds to the sum of the distance travelled along the phylogeny branch divided by the sum of the branch duration (Fig. 2C). We found significant temporal variation in HPAI spread velocity, with an overall mean of 10.5 km/week (95% high posterior density interval, HPD: 6.1–16.5). The velocity peaked in mid-December 2020, reaching a maximum of 27.8 km/week (95% HPD: 14.2–49.5). After this peak, the velocity decreased in 2021 to an average of 9.5 km/week (95% HPD: 1.4–12.3).

## Estimates of HPAI transmission dynamics between farms

We inferred epidemiological parameters based on genetic sequences from the French dataset using a GLM extended birth-death-sampling model (phylodynamic model). The infectious period of poultry farms (defined as the time between infection and suspicion dates) was estimated at a median of 9.1 days (95% HPD: 7.8–10.5). The estimated effective reproduction number ($R_e$) between farms (defined as the average number of infected farms infected by one infectious farm) over the study period showed significant temporal variation (Fig. 2D). Generally, the peak $R_e$ occurred in mid-December 2020, with $R_e$ values significantly above 1 (median: 3.8; 95% HPD: 2.0–7.7), indicating heightened transmission risk during this period. Following this peak, $R_e$ constantly decreased until end-January 2021 and reached values significantly below 1 during the month of January 2021. From early February to March 2021, $R_e$ showed some resurgence in transmission, with values settled around 1. In addition to the phylodynamic

approach, we estimated $R_e$ based on epidemiological data only (epidemiological model) (Fig. 2D). The pattern generally aligns with the trend observed with the phylodynamic model, although small differences were noted in the magnitude and precision of the $R_e$ estimates.

## Predictors associated with HPAI transmission between farms

We assessed the contribution of predictors on the weekly $R_e$ between farms based on genetic sequences from the French dataset using a GLM extended birth-death-sampling model. The inclusion probabilities and corresponding Bayes factors (BF) were used to evaluate the strength of each predictor's contribution to the model. Two predictors, the number of active duck farms and active chicken farms (within a 10 km radius of an outbreak in the 7 days before the outbreak was reported) showed a decisive contribution (BF > 100) (Fig. 3A). Note that *active* refers to farms with poultry present (excluding those that are empty due to sanitary procedures between production cycles, or due to culling measures, if they become infected or preventively culled). The density of active duck farms was positively associated with between-farm transmission, while the density of active chicken farms was negatively associated. The mean distance between active poultry farms and the percentage of water surface areas (within a 10 km radius of an outbreak in the 7 days before the outbreak was reported) indicated a strong contribution (BF > 10), with percentages of water surface areas positively associated and distances between active poultry farms negatively associated with between-farm transmission. Note that *poultry* refers to either ducks or chickens. The mean rainfall (7 days before the outbreak was reported) was not found as a significant predictor. Similar patterns were obtained when
<figure><img src="image_11.png" /><figcaption>**Fig. 3** | Drivers of HPAI H5N8 clade 2.3.4.4b transmission between farms in southwestern France, 2020–21. The left and middle panel A shows the inclusion probability of the tested predictors for the weekly estimates of the effective reproductive number ($R_e$) from the extended GLM birth-death-sampling model. This probability represents the proportion of the posterior samples in which each predictor was included in the model. Bayes Factors (BF) were used to determine the contribution of each predictor in the generalised linear model (GLM). BF quantify the likelihood of the posterior inclusion probability compared to the prior inclusion probability for each predictor. A cutoff of 3.2, 10 and 100 was used to indicate</figcaption></figure>

substantial, strong and decisive contribution of a predictor in the GLM, respectively⁵⁷. It also shows the log conditional median coefficients (points) and 95% highest posterior density interval (horizontal bars) for the predictors of $R_e$, representing the (log) contribution of each predictor when it was included in the model. The right panel B shows the farm density and proximity thresholds to mitigate HPAI H5N8 clade 2.3.4.4b transmission between poultry farms in southwestern France, 2020 and 2021, with the predicted high 95% HPD $R_e$ values as a function of the number of active duck farms and the mean distance between active poultry farms (km). The black lines indicate increases in $R_e$ values by increments of 1.

defining the predictors with a 12-day period instead of a 7-day period (Supplementary Fig. 1).

## Density and proximity thresholds limiting HPAI transmission between farms

From the associated predictors, we focused on those that can inform management actions, specifically the number of active duck farms and poultry farm proximity. Using the posterior coefficients of these two predictors, we estimated the effective reproduction number ($R_e$) between farms (Fig. 3B) and identified thresholds that ensure $R_e < 1$. Our results indicate that in areas with a high number of active duck farms, greater distances between poultry farms are necessary to reduce transmission risks.

## Discussion

Our study presents a comprehensive phylodynamic analysis of the transmission dynamics of the HPAI H5N8 (clade 2.3.4.4b) virus in southwestern France in 2020 and 2021. All poultry farm outbreaks included in our analysis formed a single monophyletic group. A single introduction from wild birds in southwestern France had already been established by previous phylogenetic investigations using both poultry and wild bird sequences²². While a common source of infection for poultry farms cannot be excluded, the strong genetic similarity among poultry viruses, combined with their spatial and temporal clustering, supports the hypothesis of sustained transmission among poultry farms, whether through direct contact or indirect mechanisms such as contaminated equipment, personnel, or local environmental exposures. The involvement of wild birds acting as bridge hosts between poultry farms can also not be excluded. The virus appears to have circulated predominantly in duck flocks in the Landes department before spreading to neighbouring areas. The transmission dynamics peaked in mid-December 2020, with an estimated velocity of 27.8 km/week and a peak effective reproduction number ($R_e$) of approximately 3.8. Notably, this $R_e$ value was higher than the peak observed during the 2016 and 2017 wave, which was estimated at around 1.5²³. This suggests that the virus was able to spread beyond the standard 3- and 10-km control and surveillance zones. This widespread transmission was likely exacerbated by the fact that infected poultry farms remained infectious for an estimated average of 9 days, before suspicion was raised, consistent with previous estimations²⁴. This delay highlights a critical window of undetected viral spread, during which infected poultry, particularly ducks, can shed high viral loads into the

environment, facilitating onward transmission. However, despite the observed velocity exceeding typical surveillance radii, the subsequent decline in $R_e$ and velocity after late December coincided with enhanced control measures, in particular extensive preventive culling, which targeted all poultry species within a 1-km radius of infected farms and all ducks and other outdoor poultry species within a 3- or 5-km radius of infected farms, likely contributing to the observed decrease in transmission²⁵. These findings suggest that while standard control and surveillance zones may be insufficient alone to contain a rapidly spreading epidemic, their effectiveness can be greatly improved when combined with timely and large-scale interventions.

We identified key factors driving the spread of the virus between poultry farms. Our results indicate that, following the introduction of the viral lineage in southwestern France, between-farm transmission was positively associated with duck farm densities in the region. This may be linked to the higher susceptibility and transmission efficiency of ducks compared to chickens for HPAI H5Nx viruses (clade 2.3.4.4b). Experimental studies have consistently shown efficient infection and environmental contamination in ducks, whereas transmission among chickens is typically less efficient²⁶⁻²⁸. This effect was even more pronounced when poultry farms were located closer to each other. However, the underlying mechanisms and their contributions to transmission in high-density areas remain unclear, as detailed data is lacking to truly test their contributions. Previous studies have shown high virus contamination in the vicinity of poultry farms, up to several hundred metres²⁹. Given the high duck farm density in the region, this suggests that contaminated dust or feathers could reach neighbouring farms. Even after cleaning and disinfection, virus particles have been detected in trucks transporting infected ducks³⁰, further suggesting that local spread could occur along transport routes. Infection by proximity may also be linked to human activity (e.g., farm workers, veterinarians, or equipment moving between farms), especially given the high connectivity between farms, or to local wildlife (e.g., commensal birds living near farms³¹) which could potentially play a bridging role. Moreover, the rapid increase in outbreaks, characterised by high numbers and short time intervals between suspicions (on average, half a day), may have overwhelmed the capacity for culling infected flocks and preventive depopulation, despite massive efforts. This resulted in delays between suspicion and culling, likely contributing to the persistence of environmental virus contamination. All of these factors underscore the territorial dimension of the
risk of transmission, where virus spread is likely driven not by a single factor but by a combination of factors, particularly in densely connected regions.

Our results also indicated that between-farm transmission was negatively associated with chicken farm density. This could not be explained by spatial segregation between species given that duck and chicken farm densities were moderately correlated (Pearson's r = 0.44, *p* < 0.001). Rather, this finding may reflect intrinsic differences in transmission potential between production types, possibly linked to differences in biosecurity measures, housing conditions, or movement patterns. Chicken farms—particularly industrial broiler or layer—are often characterised by higher biosecurity standards, stricter control of entry and exit points, limited outdoor access, and less frequent live-animal movements compared to duck farms³². These factors may reduce the risk of transmission even in densely populated areas.

Heavy rainfall in winter 2020 and 2021 caused prolonged water retention in outdoor areas, creating ideal conditions for virus survival²⁵. While this raised concerns about water runoff facilitating virus spread between farms, our analysis using rainfall as a proxy did not show a significant association with transmission. It is however possible that our rainfall data did not fully capture the complexity of water runoff dynamics, warranting further investigation. In contrast, the percentage of water surface areas, used as proxy of wild bird densities, was positively associated with between-farm transmission, potentially reflecting the involvement of wild birds as bridge hosts between poultry farms³³. However, only a small number of wild birds (<20), mostly large species like swans and geese, were reported infected by HPAI H5N8 in France in 2020–21²². This raises the possibility that other species, such as smaller birds like passerines, that are less likely to be found if dead or are not targeted by the surveillance system³⁴, could play a role in the virus transmission between farms. Although virological studies conducted in southwestern France on commensal bird species, like passerines, corvids or egrets, did not detect any HPAI virus, limitations such as optimal timing of sampling or failure to capture infected individuals prevent definitive conclusions on their role. Incorporating more specific data on wild bird species densities, movements, and improved surveillance – particularly targeting smaller and under-sampled species, could help clarify their potential role in virus spread and enhance future outbreak predictions³⁵,³⁶. Other factors influencing disease spread could not be assessed in this study. These included personnel and equipment movement, which should be further investigated. For now, raising awareness among farm workers, veterinarians and technicians about biosecurity measures when moving between farms is essential. Although poultry movements were not included here, previous studies in France have shown a limited impact³⁷,³⁸. Additional data on flock size and farming practices (e.g., outdoor versus indoor systems) were unavailable but remain critical for future research. Notably, in late 2020, many exemptions were granted to compulsory indoor housing requirements, due to logistical and economic challenges. However, efforts are still ongoing to better account for species and production-specific constraints while adapting measures to the level of risk, reflecting the continued search for practical and collaborative solutions with farmers.

Our study provides evidence-based thresholds for farm density and proximity to prevent virus spread within the country, complementing existing farm-level biosecurity measures. This offers actionable guidance to support regional biosecurity and rethink the spatial distribution and organisation of poultry farms¹². These findings are particularly relevant for regions with high poultry farm densities and outdoor farming practices, which inherently increase the risk of sustained transmission once the virus is introduced. Moving forward, implementing these thresholds may pose challenges, but short-term solutions like increasing downtime between production cycles or limiting poultry placements during high-risk periods, could help reduce transmission. In the long term, structural changes like promoting new production sites in low-density areas or enforcing regulatory frameworks for minimal farm distances, such as Italy's recent Ministry of Health decree³⁹, may be necessary. In southwestern France, the implementation of a temporary depopulation period under the poultry industry-driven “Plan Adour” between December 15, 2022 and January 15,

2023, was associated with a significant reduction in outbreaks: from 331 outbreaks in winter 2021–2022 to 20 in winter 2022–2023, representing a decrease of over 90%⁹. Vaccination has also contributed to a sharp decline in outbreaks since late 2023. A recent modelling assessment predicted that duck vaccination in 2023–2024 was likely associated with a reduction in outbreaks: from 1374 outbreaks in winter 2021–2022 and 396 in winter 2022–2023 to 10 in 2023–24, a decrease of over 90%¹⁰. These examples illustrate that both vaccination and spatial density reduction strategies can be effective tools to prevent large-scale outbreaks. Rather than being mutually exclusive, spatial thresholds and vaccination offer complementary approaches: spatial management may help limit the scale and speed of transmission once the virus is introduced, while vaccination can reduce flock susceptibility and virus shedding. However, both strategies entail significant economic and logistic trade-offs. Vaccination involves logistical challenges, mandatory surveillance protocols to monitor immune coverage, and potential trade restrictions, while spatial measures may constrain production capacity or require long-term restructuring of poultry industry, with impacts on farmers’ livelihoods and supply chains. As such, their implementation must be accompanied by discussions on financial support and compensation frameworks, in close collaboration with public authorities. Further socioeconomic evaluations are needed to assess the feasibility, acceptability, and cost-effectiveness of these complementary approaches.

## Methods

### Selection and curation of viral sequences

A total of 475 poultry farm outbreaks of HPAI H5N8 were reported from December 2, 2020 to March 20, 2021 in southwestern France (Fig. 1). During this period, the National Reference Laboratory for Avian Influenza (LNR IA, Anses-Ploufragan-Plouzane-Niort) generated a total of 432 sequences of HA segment²². To identify introduction events into France, we merged and aligned these sequences with those available on GISAID in Europe from September 7, 2020 to May 1, 2021 (n = 613 sequences) using MAFFT v7.4.9⁴⁰. After subsampling to remove identical sequences with the same sampling date, species and location, we inferred a maximum likelihood phylogenetic tree using the HKY + Γ₄ nucleotide substitution model using RAxML-NG⁵³⁴¹.

### Reconstructing the spatio-temporal dispersal history of viral lineages

To infer spatially and temporally referenced phylogenetic trees, we performed a continuous phylogeographic analysis using BEAST v1.10.4²² and the BEAGLE library¹³ to improve computational performance. The tree prior was specified as a skygrid coalescent model. The model was coupled with a HKY + Γ₄ nucleotide substitution model¹⁴ and an uncorrelated relaxed molecular clock following a lognormal prior distribution⁴⁵. The relaxed random walk (RRW) diffusion model⁴⁶ was used to perform the continuous phylogeographic reconstruction using the geographical coordinates of the sequences. Analyses were run for 100 million steps across three independent Markov chains, with states sampled every 100,000 steps. Convergence and mixing properties were assessed using Tracer v1.7.1⁷. After discarding 10% of sampled posterior trees as burn-in, the maximum clade credibility (MCC) tree was generated using TreeAnnotator v1.10.4²² and annotated using the “ggtree” package in R v4.2.2⁴⁸. The spatiotemporal information embedded within the posterior trees was extracted and visualised using the “seraphim” package⁴⁹.

### Quantifying the between-farm transmission dynamics of viral lineages

To infer the between-farm effective reproductive number ($R_e$) over time, we performed a phylodynamic analysis using BEAST v2.7.5⁵⁰ and the BEAGLE library⁴³. This parameter was defined as the average number of secondary poultry farms infected by one infectious poultry farm. The tree prior was specified as a single-type birth-death-sampling model using the “BDMM-Prime” package (https://github.com/tgvaughan/BDMM-Prime)⁵¹⁻⁵³. The model assumes a constant $R_e$ before December 12, 2020, after February 27,
2021 and infers a weekly $R_e$ estimates between those dates. We assumed that no poultry farm outbreaks went unobserved, considering the severity of the clinical signs, especially in ducks, and the recurrent nature of HPAI waves, raising farmers' awareness. Thus, the sampling proportion was informed by the number of sequences over the number of reported poultry farm outbreaks. The infectious duration of farm units (e.g., the time from farm infection to sampling/culling) was approximated by a prior distribution around a median of 7 days to match previous epidemiological estimates²³,²⁴. It was assumed that once sampled, a given farm could not be infected and sampled again; since infected poultry farms were subject to culling following the confirmation of infection. The origin of the epidemic was approximated by a prior distribution around 7 days prior to the first reported outbreak to align with previous epidemiological estimates²³,²⁴. The model was coupled with a HKY + $Γ_4$ nucleotide substitution model⁴⁴ and a lognormally distributed uncorrelated relaxed molecular clock⁴⁵. More details about the prior values and distributions of the model parameters are described in Supplementary Table 1. Analyses were run for 2 billion steps across three independent Markov chains, with states sampled every 200,000 steps. Convergence and mixing properties were assessed using Tracer v1.7.2⁴⁷. After discarding 10% of sampled posterior trees as burn-in, weekly $R_e$ estimates were plotted over time and compared to those previously estimated based on epidemiological data only⁸ using the “EpiEstim” package⁵⁴ in R v4.2.2⁶¹.

## Testing the impact of drivers on the between-farm transmission of viral lineages

To investigate the impact of specific predictors on the between-farm effective reproductive number ($R_e$), the single-type birth-death-sampling model was extended with a GLM using the GLMPrior package (https://github.com/cecivale/GLMPrior). In this parametrization, the weekly $R_e$ parameter acts as the outcome to a log-linear function of weekly mean predictor values. Predictors were: (i) the number of active duck farms, defined as the weekly average number of duck farms within a 10-km radius of outbreaks reported that week and in the 7 days before they were reported. Farms were considered active when poultry was present, based on poultry flock movement dates (when flocks were allocated to buildings), routine sanitary downtime dates (when buildings are empty between production cycles) and culling dates (when flocks are removed due to infection or preventive culling). The 7-day period was based on the average time it takes to detect infected farms²³,²⁴ and the 10-km distance aligned with the radius of the regulatory control zones in where specific measures are implemented to prevent further disease spread, such as movement restrictions, enhanced surveillance, and biosecurity protocols. Sensitivity analyses also considered a 12-day period (source: DGAl). (ii) the number of active chicken farms, which is similar to the active duck farms, e.g., the weekly average number of chicken farms with poultry present within a space-time window of 10 km and 7 days prior to outbreak reporting (source: DGAl). (iii) the mean distances between active poultry farms, which measures the weekly average distance between all poultry farms (both duck and chicken) that were active within a space-time window of 10 km and 7 days prior to outbreak reporting (source: DGAl). (iv) the mean percentage of water surface areas, which represents the weekly average percentage of areas covered by water bodies (such as ponds, rivers) within a 10-km radius of outbreaks, serving as an indicator of the presence of wild aquatic birds (source: Système d'Information sur l'Éau, https://www.data.gouv.fr/fr/datasets/cours-deau-metropole-2017-bd-carthage/). (v) the mean rainfall, which refers to the weekly average amount of rainfall in millimetres reported by the closest weather station to each outbreak and in the 7 days prior to outbreak reporting (source: Météo-France https://meteo.data.gouv.fr/). Temporal distribution of the weekly mean predictor values can be found in Supplementary Figs. 2 and 3. Following collinearity assessment among predictors, all predictors were kept given that all absolute values of the Pearson correlation were below 0.7⁵⁵.

For each predictor, the GLM parameterisation also includes a regression coefficient, which quantifies the (log) contribution of the predictors and a binary indicator variable indicating whether the predictor is included in the model. Averaging that binary variable across all steps quantifies the

probability of each predictor to be included in the model. To reduce the effect of different predictors' magnitude, all non-binary predictors were log-transformed and standardised before inclusion in the GLM. BF were used to determine the contribution of each predictor in the GLM¹³,⁵⁶,⁵⁷. BF were calculated for each predictor to quantify which of the posterior and prior inclusion probabilities of the given predictor in the model is more likely. A cutoff of 3.2, 10, and 100 was used to indicate substantial, strong and decisive contribution of a predictor in the GLM, respectively⁵⁶, meaning that its posterior inclusion probability in the model was 3.2-, 10- or 100-fold more likely than its prior inclusion probability (0.50), respectively. To provide practical insights, predictions of the between-farm effective reproductive number ($R_e$) were made based on key predictors showing potential for actionable interventions, using their posterior coefficients when they were the only predictors included in the model.

## Statistics and reproducibility

All statistical analyses were conducted in R v4.2.2⁶¹. Collinearity among predictors was assessed using Pearson correlation coefficients⁵⁵. Phylogeographic and phylodynamic inferences were performed using a Bayesian framework in BEAST v1.10.4⁴² and BEAST v2.7.5⁵⁰. All analyses were based on a dataset of 381 genetic sequences of HPAI H5N8 viruses (clade 2.3.4.4b) sampled from poultry farms in winter 2020–2021 in southwestern France. At least three independent MCMC runs were conducted to confirm convergence. The XML input files can be found at https://doi.org/10.5281/zenodo.16026353⁵⁸ to ensure reproducibility. The genetic sequences are available from the GISAID database (http://www.gisaid.org) and from the French national reference laboratory for avian influenza viruses (https://www.anses.fr), with accession numbers listed at https://doi.org/10.5281/zenodo.16026353⁵⁸.

## Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

## Data availability

All HPAI outbreaks data are available on the global animal disease information system from the Food and Agriculture Organisation of the United Nations database (https://empres-i.apps.fao.org). Numerical source data used to generate Fig. 1A can be found in Supplementary Data file 1. All HPAI genetic sequences data used in this study are available on the GISAID database (http://www.gisaid.org) and upon request from the French national reference laboratory for avian influenza viruses (https://www.anses.fr). The accession numbers in GISAID can be found at https://doi.org/10.5281/zenodo.16026353⁵⁸ and from the Supplementary Data file 2. Poultry farm structure and outbreak management data are available upon request from the French Ministry of Agriculture and Food (https://agriculture.gouv.fr/french-ministry-agriculture-and-food). Climatic and landscape data are accessible via Météo-France (https://meteo.data.gouv.fr/) and Système d'Information sur l'Éau (https://www.data.gouv.fr/fr/datasets/cours-deau-metropole-2017-bd-carthage/) databases. Numerical source data used to generate Supplementary Figs. 2 and 3 can be found in Supplementary Data file 3. Numerical source data used to generate Fig. 2C-D and Fig. 3A can be found in Supplementary Data files 4 and 5. All numerical source data files can be found at https://doi.org/10.5281/zenodo.16026353⁵⁸.

## Code availability

The XML files used to perform the phylogeographic and phylodynamic analyses can be found at https://doi.org/10.5281/zenodo.16026353⁵⁸.

Received: 10 April 2025; Accepted: 7 August 2025; Published online: 29 August 2025

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## Acknowledgements

We thank the veterinarians and farmers involved in samples collection, the local official laboratories for initial samples screening, the local and central veterinary services (Directions Départementales en charge de la Protection des Populations, Direction Générale de l'Alimentation) involved in the HPAI outbreak management and the National Reference Laboratory for Avian Influenza viruses (LNR AI ANSES-Ploufragan-Plouzané-Niort) for the

sequencing process. The authors gratefully acknowledge the contributions to GISAID from other laboratories: Animal and Plant Health Agency, Erasmus Medical Centre, Friedrich-Loeffler-Institut, Istituto Zooprofilattico Sperimentale Delle Venezie, National Food Chain Safety Office, National Veterinary Institute, National Veterinary Research Institute Poland, Sciensano, State Veterinary Institute Prague, Statens Serum Institute, Wageningen Bioveterinary Research. This work was financially supported by the FEDER/Région Occitanie Recherche et Sociétés 2018—AI-TRACK and the PREDYT project (Fonds pour la Recherche sur l'Influenza Aviaire). This study was also supported by the “Chair for Avian Biosecurity”, hosted by the National Veterinary School of Toulouse and funded by the Direction Générale de l'Alimentation, France.

## Author contributions

Study design: C.G., D.C., T.V. Data resources: F-X.B., B.G., L.F. Data curation: F-X.B., L.F. Data analysis: C.G., S.L. Package resources: C.V.A., T.S. Analysis guidance: C.V.A., T.S., L.D.P. Manuscript preparation: C.G. Review and approval of final manuscript: C.G., C.V.A., F-X.B., D.C., L.F., S.L., A.J.P., S.R., G.G., L.D.P., T.S., B.G., M.C.P. and T.V.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s42003-025-08687-4.

**Correspondence** and requests for materials should be addressed to Claire Guinat.

**Peer review information** *Communications Biology* thanks Luca Bordes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Pei Hao and David Favero. A peer review file is available.

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