Spatial risk modelling of highly pathogenic avian influenza in France: Fattening duck farm activity matters

In this study, we present a comprehensive analysis of the key spatial risk factors and predictive risk maps for HPAI infection in France, with a focus on the 2016-17 and 2020-21 epidemic waves. Our findings indicate that the most explanatory spatial predictor variables were related to fattening duck movements prior to the epidemic, which should be considered as indicators of farm operational status, e.g., whether they are active or not. Moreover, we found that considering the operational status of duck houses in nearby municipalities is essential for accurately predicting the risk of future HPAI infection. Our results also show that the density of fattening duck houses could be used as a valuable alternative predictor of the spatial distribution of outbreaks per municipality, as this data is generally more readily available than data on movements between houses. Accurate data regarding poultry farm densities and movements is critical for developing accurate mathematical models of HPAI virus spread and for designing effective prevention and control strategies for HPAI. Finally, our study identifies the highest risk areas for HPAI infection in southwest and northwest France, which is valuable for informing national risk-based strategies and guiding increased surveillance efforts in these regions.

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Publication Details

Journal
PLoS ONE
Published
2025-02-04
DOI
https://doi.org/10.1371/journal.pone.0316248
Primary Topic
Animal Disease Management and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatial risk modelling of highly pathogenic avian influenza in France: Fattening duck farm activity matters

Jean Artois, Jean‐Luc Guérin, Lisa Fourtune, Claire Guinat et al.
PLoS ONE
Animal Disease Management and Epidemiology
article

Spatial risk modelling of highly pathogenic avian influenza in France: Fattening duck farm activity matters

Jean Artois, Jean‐Luc Guérin, Lisa Fourtune, Claire Guinat, Simon Dellicour, Timothée Vergne, Mathilde Paul, Axelle Scoizec, Sophie Le Bouquin
article en

Abstract

In this study, we present a comprehensive analysis of the key spatial risk factors and predictive risk maps for HPAI infection in France, with a focus on the 2016-17 and 2020-21 epidemic waves. Our findings indicate that the most explanatory spatial predictor variables were related to fattening duck movements prior to the epidemic, which should be considered as indicators of farm operational status, e.g., whether they are active or not. Moreover, we found that considering the operational status of duck houses in nearby municipalities is essential for accurately predicting the risk of future HPAI infection. Our results also show that the density of fattening duck houses could be used as a valuable alternative predictor of the spatial distribution of outbreaks per municipality, as this data is generally more readily available than data on movements between houses. Accurate data regarding poultry farm densities and movements is critical for developing accurate mathematical models of HPAI virus spread and for designing effective prevention and control strategies for HPAI. Finally, our study identifies the highest risk areas for HPAI infection in southwest and northwest France, which is valuable for informing national risk-based strategies and guiding increased surveillance efforts in these regions.

PLoS ONEVol. 20(2)
Université Libre de Bruxelles (BE), École Nationale Vétérinaire de Toulouse (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Rega Institute for Medical Research (BE), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Walloon Agricultural Research Centre (BE), Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail (FR), Interactions hôtes-agents pathogènes (FR), KU Leuven (BE)
Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail
Good health and well-being
Openalex Percentile: Top 98%
Animal Disease Management and Epidemiology
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