Different environmental gradients associated to the spatiotemporal and genetic pattern of the H5N8 highly pathogenic avian influenza outbreaks in poultry in Italy

Vaccination of poultry against highly pathogenic avian influenza - part 1. Available vaccines and vaccination strategies

Several vaccines have been developed against highly pathogenic avian influenza (HPAI), mostly inactivated whole-virus vaccines for chickens. In the EU, one vaccine is authorised in chickens but is not fully efficacious to stop transmission, highlighting the need for vaccines tailored to diverse poultry species and production types. Off-label use of vaccines is possible, but effectiveness varies. Vaccines are usually injectable, a time-consuming process. Mass-application vaccines outside hatcheries remain rare. First vaccination varies from in-ovo to 6 weeks of age. Data about immunity onset and duration in the target species are often unavailable, despite being key for effective planning. Minimising antigenic distance between vaccines and field strains is essential, requiring rapid updates of vaccines to match circulating strains. Generating harmonised vaccine efficacy data showing vaccine ability to reduce transmission is crucial and this ability should be also assessed in field trials. Planning vaccination requires selecting the most adequate vaccine type and vaccination scheme. Emergency protective vaccination is limited to vaccines that are not restricted by species, age or pre-existing vector-immunity, while preventive vaccination should prioritise achieving the highest protection, especially for the most susceptible species in high-risk transmission areas. Model simulations in France, Italy and The Netherlands revealed that (i) duck and turkey farms are more infectious than chickens, (ii) depopulating infected farms only showed limitations in controlling disease spread, while 1-km ring-culling performed better than or similar to emergency preventive ring-vaccination scenarios, although with the highest number of depopulated farms, (iii) preventive vaccination of the most susceptible species in high-risk transmission areas was the best option to minimise the outbreaks' number and duration, (iv) during outbreaks in such areas, emergency protective vaccination in a 3-km radius was more effective than 1- and 10-km radius. Vaccine efficacy should be monitored and complement other surveillance and preventive efforts.

Combined Phylogeographic Analyses and Epidemiologic Contact Tracing to Characterize Atypically Pathogenic Avian Influenza (H3N1) Epidemic, Belgium, 2019

The high economic impact and zoonotic potential of avian influenza call for detailed investigations of dispersal dynamics of epidemics. We integrated phylogeographic and epidemiologic analyses to investigate the dynamics of a low pathogenicity avian influenza (H3N1) epidemic that occurred in Belgium during 2019. Virus genomes from 104 clinical samples originating from 85% of affected farms were sequenced. A spatially explicit phylogeographic analysis confirmed a dominating northeast to southwest dispersal direction and a long-distance dispersal event linked to direct live animal transportation between farms. Spatiotemporal clustering, transport, and social contacts strongly correlated with the phylogeographic pattern of the epidemic. We detected only a limited association between wind direction and direction of viral lineage dispersal. Our results highlight the multifactorial nature of avian influenza epidemics and illustrate the use of genomic analyses of virus dispersal to complement epidemiologic and environmental data, improve knowledge of avian influenza epidemiologic dynamics, and enhance control strategies.

An integrated system for the management of environmental data to support veterinary epidemiology

Environmental and climatic fluctuations can greatly influence the dynamics of infectious diseases of veterinary concern, or interfere with the implementation of relevant control measures. Including environmental and climatic aspects in epidemiological studies could provide policy makers with new insights to assign resources for measures to prevent or limit the spread of animal diseases, particularly those with zoonotic potential. The ever-increasing number of technologies and tools permits acquiring environmental data from various sources, including ground-based sensors and Satellite Earth Observation (SEO). However, the high heterogeneity of these datasets often requires at least some basic GIS (Geographic Information Systems) and/or coding skills to use them in further analysis. Therefore, the high availability of data does not always correspond to widespread use for research purposes. The development of an integrated data pre-processing system makes it possible to obtain information that could be easily and directly used in subsequent epidemiological analyses, supporting both research activities and the management of disease outbreaks. Indeed, such an approach allows for the reduction of the time spent on searching, downloading, processing and validating environmental data, thereby optimizing available resources and reducing any possible errors directly related to data collection. Although multitudes of free services that allow obtaining SEO data exist nowadays (either raw or pre-processed through a specific coding language), the availability and quality of information can be sub-optimal when dealing with very small scale and local data. In fact, some information sets (e.g., air temperature, rainfall), usually derived from ground-based sensors (e.g., agro-meteo station), are managed, processed and redistributed by agencies operating on a local scale which are often not directly accessible by the most common free SEO services (e.g., Google Earth Engine). The EVE (Environmental data for Veterinary Epidemiology) system has been developed to acquire, pre-process and archive a set of environmental information at various scales, in order to facilitate and speed up access by epidemiologists, researchers and decision-makers, also accounting for the integration of SEO information with locally sensed data.

A review of estimated transmission parameters for the spread of avian influenza viruses

Avian influenza poses an increasing problem in Europe and around the world. Simulation models are a useful tool to predict the spatiotemporal risk of avian influenza spread and evaluate appropriate control actions. To develop realistic simulation models, valid transmission parameters are critical. Here, we reviewed published estimates of the basic reproduction number (R₀ ), the latent period and the infectious period by virus type, pathogenicity, species, study type and poultry flock unit. We found a large variation in the parameter estimates, with highest R₀ estimates for H5N1 and H7N3 compared with other types; for low pathogenic avian influenza compared with high pathogenic avian influenza types; for ducks compared with other species; for estimates from field studies compared with experimental studies; and for within-flock estimates compared with between-flock estimates. Simulation models should reflect this observed variation so as to produce more reliable outputs and support decision-making. How to incorporate this information into simulation models remains a challenge.

High altitude Relieves transmission risks of COVID-19 through meteorological and environmental factors: Evidence from China

Existing studies reported higher altitudes reduce the COVID-19 infection rate in the United States, Colombia, and Peru. However, the underlying reasons for this phenomenon remain unclear. In this study, regression analysis and mediating effect model were used in a combination to explore the altitudes relation with the pattern of transmission under their correlation factors. The preliminary linear regression analysis indicated a negative correlation between altitudes and COVID-19 infection in China. In contrast to environmental factors from low-altitude regions (<1500 m), high-altitude regions (>1500 m) exhibited lower PM2.5, average temperature (AT), and mobility, accompanied by high SO₂ and absolute humidity (AH). Non-linear regression analysis further revealed that COVID-19 confirmed cases had a positive correlation with mobility, AH, and AT, whereas negatively correlated with SO₂, CO, and DTR. Subsequent mediating effect model with altitude-correlated factors, such as mobility, AT, AH, DTR and SO₂, suffice to discriminate the COVID-19 infection rate between low- and high-altitude regions. The mentioned evidence advance our understanding of the altitude-mediated COVID-19 transmission mechanism.

Wild Bird Densities and Landscape Variables Predict Spatial Patterns in HPAI Outbreak Risk across The Netherlands

Highly pathogenic avian influenza viruses’ (HPAIVs) transmission from wild birds to poultry occurs globally, threatening animal and public health. To predict the HPAI outbreak risk in relation to wild bird densities and land cover variables, we performed a case-control study of 26 HPAI outbreaks (cases) on Dutch poultry farms, each matched with four comparable controls. We trained machine learning classifiers to predict outbreak risk with predictors analyzed at different spatial scales. Of the 20 best explaining predictors, 17 consisted of densities of water-associated bird species, 2 of birds of prey, and 1 represented the surrounding landscape, i.e., agricultural cover. The spatial distribution of mallard (Anas platyrhynchos) contributed most to risk prediction, followed by mute swan (Cygnus olor), common kestrel (Falco tinnunculus) and brant goose (Branta bernicla). The model successfully distinguished cases from controls, with an area under the receiver operating characteristic curve of 0.92, indicating accurate prediction of HPAI outbreak risk despite the limited numbers of cases. Different classification algorithms led to similar predictions, demonstrating robustness of the risk maps. These analyses and risk maps facilitate insights into the role of wild bird species and support prioritization of areas for surveillance, biosecurity measures and establishments of new poultry farms to reduce HPAI outbreak risks.

Different environmental gradients associated to the spatiotemporal and genetic pattern of the H5N8 highly pathogenic avian influenza outbreaks in poultry in Italy

Comprehensive understanding of the patterns and drivers of avian influenza outbreaks is pivotal to inform surveillance systems and heighten nations' ability to quickly detect and respond to the emergence of novel viruses. Starting in early 2017, the Italian poultry sector has been involved in the massive H5N8 highly pathogenic avian influenza epidemic that spread in the majority of the European countries in 2016/2017. Eighty-three outbreaks were recorded in north-eastern Italy, where a densely populated poultry area stretches along the Lombardy, Emilia-Romagna and Veneto regions. The confirmed cases, affecting both the rural and industrial sectors, depicted two distinct epidemic waves. We adopted a combination of multivariate statistics techniques and multi-model regression selection and inference, to investigate how environmental factors relate to the pattern of outbreaks diversity with respect to their spatiotemporal and genetic diversity. Results showed that a combination of eco-climatic and host density predictors were associated with the outbreaks pattern, and variation along gradients was noticeable among genetically and geographically distinct groups of avian influenza cases. These regional contrasts may be indicative of a different mechanism driving the introduction and spreading routes of the influenza virus in the domestic poultry population. This methodological approach may be extended to different spatiotemporal scale to foster site-specific, ecologically informed risk mitigating strategies.