tetano
Editor, Senior Moderator
Front Vet Sci
. 2025 Jun 13:12:1541019.
doi: 10.3389/fvets.2025.1541019. eCollection 2025. Investigating the role of environmental factors in the French highly pathogenic avian influenza epizootic in 2022-2023
Maryem Ben Salem[SUP] 1 [/SUP], Mathieu Andraud[SUP] 1 [/SUP], Stéphanie Bougeard[SUP] 1 [/SUP], Virginie Allain[SUP] 1 [/SUP], Morgane Salines[SUP] 1 [/SUP], Rodolphe Thomas[SUP] 1 [/SUP], Audrey Schmitz[SUP] 2 [/SUP], Legrand Saint-Cyr[SUP] 3 [/SUP], Karine Fiore[SUP] 3 [/SUP], Sophie Le Bouquin[SUP] 1 [/SUP], Axelle Scoizec[SUP] 1 [/SUP]
Affiliations
Introduction: The recurring epizootics of highly pathogenic avian influenza (HPAI) in France have been associated with changes in the epidemiological landscape, such as higher frequency of detections in wild birds and introductions into backyard farms. This highlights the need for a deeper understanding of the factors that drive the spread of HPAI, particularly environmental ones, which, unlike other factors, are still understudied.
Methods: In this study, we examined various farm and environmental variables around the 2022-2023 outbreak sites in France to unravel potential common traits among detected outbreaks. From August 2022 to March 2023, 397 poultry farms were infected, including different species and production types. For each outbreak, the farm characteristics and variables related with their direct environment within a 2 km radius were collected. Based on the Gower distance, accounting for qualitative and quantitative variable, clusters were identified using k-medoid partitioning algorithm. A random forest analysis was further used to hierarchize the relative role of each variable in the clustering process, to assess the importance of the farm structural and environmental conditions on the outbreak occurrence. To disentangle the impact of environmental factors from intrinsic herd characteristics, the method was applied twice: first, using the whole dataset including the farm characteristics and environmental variables (first scenario); second, accounting exclusively for the environmental variables (second scenario).
Results: Overall, farm variables such as farm type were crucial in the clustering process, overpassing most of the environmental factors, although the distance from "particular risk zones" and the coastline were also important. However, the clusters obtained with the second scenario that counts only for the environmental variables, remained consistent with the first scenario.
Discussion: This shows a non-negligible impact of environmental conditions on the probability of viral introduction in poultry herds. This study used an innovative approach to explore how HPAI dynamics can be influenced by external factors, which could help in the design of risk zones at the national level.
Keywords: classification; clustering; epidemiology; machine learning; poultry; random forest.
. 2025 Jun 13:12:1541019.
doi: 10.3389/fvets.2025.1541019. eCollection 2025. Investigating the role of environmental factors in the French highly pathogenic avian influenza epizootic in 2022-2023
Maryem Ben Salem[SUP] 1 [/SUP], Mathieu Andraud[SUP] 1 [/SUP], Stéphanie Bougeard[SUP] 1 [/SUP], Virginie Allain[SUP] 1 [/SUP], Morgane Salines[SUP] 1 [/SUP], Rodolphe Thomas[SUP] 1 [/SUP], Audrey Schmitz[SUP] 2 [/SUP], Legrand Saint-Cyr[SUP] 3 [/SUP], Karine Fiore[SUP] 3 [/SUP], Sophie Le Bouquin[SUP] 1 [/SUP], Axelle Scoizec[SUP] 1 [/SUP]
Affiliations
- PMID: 40586036
- PMCID: PMC12202217
- DOI: 10.3389/fvets.2025.1541019
Introduction: The recurring epizootics of highly pathogenic avian influenza (HPAI) in France have been associated with changes in the epidemiological landscape, such as higher frequency of detections in wild birds and introductions into backyard farms. This highlights the need for a deeper understanding of the factors that drive the spread of HPAI, particularly environmental ones, which, unlike other factors, are still understudied.
Methods: In this study, we examined various farm and environmental variables around the 2022-2023 outbreak sites in France to unravel potential common traits among detected outbreaks. From August 2022 to March 2023, 397 poultry farms were infected, including different species and production types. For each outbreak, the farm characteristics and variables related with their direct environment within a 2 km radius were collected. Based on the Gower distance, accounting for qualitative and quantitative variable, clusters were identified using k-medoid partitioning algorithm. A random forest analysis was further used to hierarchize the relative role of each variable in the clustering process, to assess the importance of the farm structural and environmental conditions on the outbreak occurrence. To disentangle the impact of environmental factors from intrinsic herd characteristics, the method was applied twice: first, using the whole dataset including the farm characteristics and environmental variables (first scenario); second, accounting exclusively for the environmental variables (second scenario).
Results: Overall, farm variables such as farm type were crucial in the clustering process, overpassing most of the environmental factors, although the distance from "particular risk zones" and the coastline were also important. However, the clusters obtained with the second scenario that counts only for the environmental variables, remained consistent with the first scenario.
Discussion: This shows a non-negligible impact of environmental conditions on the probability of viral introduction in poultry herds. This study used an innovative approach to explore how HPAI dynamics can be influenced by external factors, which could help in the design of risk zones at the national level.
Keywords: classification; clustering; epidemiology; machine learning; poultry; random forest.