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The severe impact of recently reported Highly Pathogenic Avian Influenza (HPAI) outbreaks have emphasized the need to better monitor biosecurity practices among the different French poultry production systems. Between October 2016 and September 2018, a large national cross-sectional study was carried out in France to assess biosecurity levels in high-risk poultry farms and identify farm biosecurity profiles, using Multiple Correspondence and Hierarchical Cluster Analyses. Results indicate that there is substantial room for improvement in cleaning and disinfection, anteroom management and delimitation of farm and production units for more than 50 % of the 1,004 analyzed farms. The farm biosecurity profile showing the highest level of biosecurity included commercial intensive poultry farms (gallinaceous poultry farms, in North-Western France), and those with a recent history of HPAI outbreak (duck farms, in South-Western France). The farms biosecurity profiles showing the lowest level of biosecurity included farms with multiple species (duck and gallinaceous poultry) or multiple production types (broilers and egg-layers), located in regions with a lower poultry density and without a recent history of HPAI outbreak. Outcomes provide support to adapt biosecurity improvement and inspection plans., (Copyright © 2021. Published by Elsevier B.V.)

The relative roles that movement and proximity networks play in the spread of highly pathogenic avian influenza (HPAI) viruses are often unknown during an epidemic, preventing effective control. We used network analysis to explore the devastating epidemic of HPAI A(H5N8) among poultry, in particular ducks, in France during 2016-2017 and to estimate the likely contribution of live-duck movements. Approximately 0.2% of live-duck movements could have been responsible for between-farm transmission events, mostly early during the epidemic. Results also suggest a transmission risk of 35.5% when an infected holding moves flocks to another holding within 14 days before detection. Finally, we found that densely connected groups of holdings with sparse connections between groups overlapped farmer organizations, which represents important knowledge for surveillance design. This study highlights the importance of movement bans in zones affected by HPAI and of understanding transmission routes to develop appropriate HPAI control strategies.

In the winter 2016-2017 the largest epidemic of highly pathogenic avian influenza (HPAI) ever recorded in the European Union spread to all 28 member states. France was hit particularly hard and reported a total of 484 infected premises (IPs) by March 2017. We developed a mathematical model to analyze the spatiotemporal evolution of the epidemic and evaluate the impact of control strategies. We estimated that farms rearing ducks were on average 2.5 times more infectious and 5.0 times more susceptible to HPAI than farms rearing other avian species. The implementation of surveillance zones around IPs reduced transmission by a factor of 1.8 on average. Compared to the strengthening of pre-emptive culling measures enforced by French authorities in February 2017, we found that a faster depopulation of diagnosed IPs would have had a larger impact on the total number of infections. For example, halving the time delay from detection to slaughter of infected animals would have reduced the total number of IPs by 52% and total cull numbers by 50% on average. This study showcases the possible contribution of modeling to inform and optimize control strategies during an outbreak., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

IntroductionFrance is one of Europe's foremost poultry producers and the world's fifth largest producer of poultry meat. In November 2016, highly pathogenic avian influenza (HPAI) virus subtype H5N8 emerged in poultry in the country. As of 23 March 2017, a total of 484 confirmed outbreaks were reported, with consequences on animal health and socio-economic impacts for producers. Methods: We examined the spatio-temporal distribution of outbreaks that occurred in France between November 2016 and March 2017, using the space-time K-function and space-time permutation model of the scan statistic test. Results : Most outbreaks affected duck flocks in south-west France. A significant space-time interaction of outbreaks was present at the beginning of the epidemic within a window of 8 km and 13 days. This interaction disappeared towards the epidemic end. Five spatio-temporal outbreak clusters were identified in the main poultry producing areas, moving sequentially from east to west. The average spread rate of the epidemic front wave was estimated to be 5.5 km/week. It increased from February 2017 and was negatively associated with the duck holding density. Conclusion : HPAI-H5N8 infections varied over time and space in France. Intense transmission events occurred at the early stages of the epidemic, followed by long-range jumps in the disease spread towards its end. Findings support strict control strategies in poultry production as well as the maintenance of high biosecurity standards for poultry holdings. Factors and mechanisms driving HPAI spread need to be further investigated.

Logistic regression models integrating disease presence/absence data are widely used to identify risk factors for a given disease. However, when data arise from imperfect surveillance systems, the interpretation of results is confusing since explanatory variables can be related either to the occurrence of the disease or to the efficiency of the surveillance system. As an alternative, we present spatial and non-spatial zero-inflated Poisson (ZIP) regressions for modelling the number of highly pathogenic avian influenza (HPAI) H5N1 outbreaks that were reported at subdistrict level in Thailand during the second epidemic wave (July 3rd 2004 to May 5th 2005). The spatial ZIP model fitted the data more effectively than its non-spatial version. This model clarified the role of the different variables: for example, results suggested that human population density was not associated with the disease occurrence but was rather associated with the number of reported outbreaks given disease occurrence. In addition, these models allowed estimating that 902 (95% CI 881-922) subdistricts suffered at least one HPAI H5N1 outbreak in Thailand although only 779 were reported to veterinary authorities, leading to a general surveillance sensitivity of 86.4% (95% CI 84.5-88.4). Finally, the outputs of the spatial ZIP model revealed the spatial distribution of the probability that a subdistrict could have been a false negative. The methodology presented here can easily be adapted to other animal health contexts., (Copyright © 2014 Elsevier B.V. All rights reserved.)

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 HPAI epidemics in 2020–21 and 2021–22. We conducted a descriptive analysis of the 2020–21 and 2021–22 epidemics, as a first step towards identifying the poultry sector's 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 the 2016–17 epidemic and to the 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 1375 outbreaks during the 2021–22 epidemic. In both epidemics, the outbreaks' distribution matched extremely well that of 2016–17, and most outbreaks (80.6% and 68.4%) were located in 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. As soon as the virus reached the high‐risk zones, it started to spread between farms at very high rates, with each infected farm infecting between two and three other farms at the peaks of transmission. 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. [ABSTRACT FROM AUTHOR]

Infectious diseases are a major burden to global economies, and public and animal health. To date, quantifying the spread of infectious diseases to inform policy making has traditionally relied on epidemiological data collected during epidemics. However, interest has grown in recent phylodynamic techniques to infer pathogen transmission dynamics from genetic data. Here, we provide examples of where this new discipline has enhanced disease management in public health and illustrate how it could be further applied in animal health. In particular, we describe how phylodynamics can address fundamental epidemiological questions, such as inferring key transmission parameters in animal populations and quantifying spillover events at the wildlife–livestock interface, and generate important insights for the design of more effective control strategies. Phylodynamics, an increasingly popular statistical framework to infer pathogen transmission dynamics from genetic data, remains overlooked in animal health research. This restricts our ability to design more effective disease control strategies. Phylodynamics can be valuable when investigating the determinants of disease spread, allowing the better targeting of animal populations for disease control and reducing the impact of epidemics. The wildlife–livestock–human interface is critical for disease emergence; thus, promoting phylodynamics at this interface should improve our ability to manage complex epidemics. There is a critical need to improve capacity-building efforts for infectious disease detection in hard-to-sample populations, such as wildlife. Collaborative efforts are needed to make genetic data publicly available in a timely manner during animal disease outbreaks. [ABSTRACT FROM AUTHOR]