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This is the third in a planned series of data papers presenting modelled vector distributions produced during the ECDC and EFSA funded VectorNet project. The data package presented here includes those Culicoides vectors species first modelled in 2015 as part of the VectorNet gap analysis work namely C. imicola, C. obsoletus, C. scoticus, C. dewulfi, C. chiopterus, C. pulicaris, C. lupicaris, C. punctatus, and C. newsteadi. The known distributions of these species within the Project area (Europe, the Mediterranean Basin, North Africa, and Eurasia) are currently incomplete to a greater or lesser degree. The models are designed to fill the gaps with predicted distributions, to provide a) first indication of vector species distributions across the project geographical extent, and b) assistance in targeting surveys to collect distribution data for those areas with no field validated information. The models are based on input data from light trap surveillance of adult Culicoides across continental Europe and surrounding regions (71.8°N –33.5°S, – 11.2°W – 62°E), concentrated in Western countries, supplemented by transect samples in eastern and northern Europe. Data from central EU are relatively sparse.

Background: Fasciola hepatica, a trematode parasite (liver fluke), infects a wide range of host species causing fasciolosis. The disease is prevalent world-wide and causes considerable economic losses to the livestock industry. Fasciolosis is regarded as an emerging food-borne zoonosis. To promote awareness among farmers and to implement strategies to control the infection, this study examined the prevalence, spatial distribution and risk factors for F. hepatica infection in Danish cattle herds. Methods: A retrospective population based study was performed using meat inspection data of approximately 1.5 million cattle slaughtered in the period 2011 to 2013. Annual cumulative prevalence of recorded liver fluke findings was calculated for each year. Global and local spatial cluster analysis was used to identify and map spatial patterns of F. hepatica positive and negative herds to explore environmental indicators of infection. Herd level, trade and environmental risk factors were evaluated for association with infection using logistic regression. Herd infection status as predicted from the final risk factor model was compared with the observed status using heat maps to assess how well the model fitted the observed spatial pattern. Results: During the investigated period (2011–2013), an increase in annual herd prevalence was noted (2011–25.6%; 2012–28.4%; 2013–29.3%). The spatial analysis suggested significant clustering of positive and negative herds. Presence of streams, wetlands and pastures on farms showed a significant association with the presence of infection in cattle herds. Buying animals from positive herds was a risk factor on conventional farms. Additionally, risk of being infected with F. hepatica was higher in non-dairy herds of medium size (=30 and <100) when compared to dairy and large (=100) cattle herds. The observed spatial pattern could be reproduced by predictions of the risk factor model. Conclusions: This study showed an increase in annual herd l

One of the aims of the DynVect project was to set up a network of European entomologists working on #Culicoides#, the vectors of bluetongue virus, to create a platform for discussion, data sharing and data analysis. The first task consisted in describing the surveillance systems in place in each country. For this, a questionnaire was sent to 9 countries: Belgium, Denmark, England, France, Germany, Italy, Spain, Sweden and The Netherlands. Between 1 and 9 institutes per country were involved in the surveillance. The first country to set up a surveillance system was Spain (in 2000), followed by other southern countries (Italy and France). A second wave of countries set up surveillance systems in 2006, with the arrival of bluetongue in northern Europe. All countries currently continue the surveillance except Germany (which stopped the surveillance in mid 2008). All countries shared common aims: defining "vector-free" periods and describing the diversity and dynamics of species. Except in Italy, all surveillance protocols evolved over the years, for example from covering specific regions to the entire country, and/or with an increasing number of traps. Surveillance systems vary between countries in terms of i) number of traps per country (5 - 300), ii) types of traps used (OVI, CDC, BG, Rieb, Rothamsted suction trap), although currently all countries use OVI traps except for Spain, iii) trapping frequency (day and night, weekly, fortnightly or monthly), iv) number of collection nights (mainly 1 night trapping, except Belgium with 2 consecutive nights and Germany with 7 consecutive days), v) trap location within farm (inside or outside buildings or both according to the season), vi) the level to which species are identified (Group, Complex or species) and vii) the definition of the vector-free period. Following this descriptive phase, participants discussed the data which could be shared and the associated aims as well as the database structure which should be used. This