bioRxivEcology
Two major epidemics of highly pathogenic avian influenza virus H5N8 and H5N1 in domestic poultry in France, 2020-2022
S. Lambert, B. Durand, M. Andraud, R. Delacoourt, A. Scoizec, S. Le Bouquin, S. Rautureau, B. Bauzile, C. Guinat, L. Fourtune, J.-L. Guerin, M. C. Paul, T. Vergne
Abstract
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.
- research focus: ecology & biodiversity
- research focus: differential expression
- research focus: spatial domain detection
- approach: statistical model
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