Overview of cattle diseases listed under category C, D or E in the animal health law for wich control programmes are in place within Europe

13 páginas, 5 figuras, 3 tablas.The COST action “Standardising output-based surveillance to control non-regulated diseases of cattle in the European Union (SOUND control),” aims to harmonise the results of surveillance and control programmes (CPs) for non-EU regulated cattle diseases to facilitate safe trade and improve overall control of cattle infectious diseases. In this paper we aimed to provide an overview on the diversity of control for these diseases in Europe. A non-EU regulated cattle disease was defined as an infectious disease of cattle with no or limited control at EU level, which is not included in the European Union Animal health law Categories A or B under Commission Implementing Regulation (EU) 2020/2002. A CP was defined as surveillance and/or intervention strategies designed to lower the incidence, prevalence, mortality or prove freedom from a specific disease in a region or country. Passive surveillance, and active surveillance of breeding bulls under Council Directive 88/407/EEC were not considered as CPs. A questionnaire was designed to obtain country-specific information about CPs for each disease. Animal health experts from 33 European countries completed the questionnaire. Overall, there are 23 diseases for which a CP exists in one or more of the countries studied. The diseases for which CPs exist in the highest number of countries are enzootic bovine leukosis, bluetongue, infectious bovine rhinotracheitis, bovine viral diarrhoea and anthrax (CPs reported by between 16 and 31 countries). Every participating country has on average, 6 CPs (min–max: 1–13) in place. Most programmes are implemented at a national level (86%) and are applied to both dairy and non-dairy cattle (75%). Approximately one-third of the CPs are voluntary, and the funding structure is divided between government and private resources. Countries that have eradicated diseases like enzootic bovine leukosis, bluetongue, infectious bovine rhinotracheitis and bovine viral diarrhoea have implemented CPs for other diseases to further improve the health status of cattle in their country. The control of non-EU regulated cattle diseases is very heterogenous in Europe. Therefore, the standardising of the outputs of these programmes to enable comparison represents a challenge.Peer reviewe

DEVELOPMENT OF RECOMBINANT POSITIVE CONTROL FOR Francisella tularensis DETECTION BY Q-PCR

The aim of the work was to construct and test the recombinant positive control for F. tularensis detection by a real-time polymerase chain reaction (qPCR). The molecular TA-cloning of pTZ57_F/R plasmid ligated with tul4 gene PCR product into DH5α E. coli. The minimal detection level in qPCR was one copy number per reaction. The obtained positive control was highly sensitive, specific and safe qPCR in the laboratory tularemia diagnostics

Suitable habitats for Palearctic Ornithodoros soft ticks

Ticks are economically and medically important due to injuries to livestock directly caused by their bite and their ability to transmit pathogens to humans and animals. While hard ticks (Ixodidae) have been extensively studied, the ability of soft ticks (Argasidae) to transmit pathogens to humans and domestic animals remains underestimated (Gray, Estrada-Pena, and Vial 2014). These ticks are nonetheless medically important, especially those belonging to the Ornithodoros genus. In the Palearctic region, five Ornithodoros tick species are considered able to transmit borreliae that cause Tick-Borne Relapsing Fever (TBRF) in humans, with many ocular, encephalitic, arthritic and pregnancy complications (Rebaudet and Parola 2006). The detection of human TBRF cases caused by Borrelia hispanica between 2002 and 2012 in Spain confirmed that the risk of human contamination through soft tick bites in Europe is real (Croche Santander et al. 2015). In the same region, the Iberian tick species O. erraticus was confirmed as a competent vector and natural reservoir for the virus of African Swine Fever (ASF) (Boinas et al. 2011). Knowing the spatial distribution of ticks is essential to assess the risk for pathogen transmission by ticks, so we set out to map suitable habitats for Ornithodoros ticks. In the Palearctic, presence data for Ornithodoros ticks are scarce and largely historical, and absence data are mostly unavailable (Vial 2009). Due to the relative scarcity of high-quality occurrence data, a GIS-based Multi-Criteria Decision Analysis was chosen to describe the range of environmental conditions enabling ticks to survive, grow and reproduce. Based on ecological knowledge on Palearctic Ornithodoros ticks distilled from an in-depth literature review, five criteria were identified. Two criteria were linked to temperatures allowing feeding activity and tick development. The three other criteria were related to the moisture availability for tick survival and development, either from precipitation, ambient humidity or other environmental factors that may provide sufficient moisture in arid zones. In order to incorporate uncertainty, a sensitivity analysis was done by performing different runs of the model and varying the environmental variables describing the respective criteria, the suitability response curves for each of the variables and the weights attributed to the different criteria for each run. Only in the final step, the available presence/absence data were used to validate the results. All models indicated similar trends. Several highly suitable areas were identified along the southern frontier between Portugal and Spain as well as the eastern coast of Spain from Catalonia to Andalusia. Also highlighted were the coastal areas in Sardinia, Sicilia and eastern Italy, as well as eastern Greece and western Turkey. To the east, the estimated distribution extended to the Ukrainian and south-west Russian region above the Black Sea. The models were based on climate variables and did not aim to assess local heterogeneity. Nonetheless, this remains one of the rare examples where a knowledge-based approach was used to produce distribution maps for ticks. This approach compensated both for the scarcity of the presence data and the fuzzy nature of the knowledge available on the ecology of these Ornithodoros tick species. The high values of the accuracy measures, as well as major congruence between the different models for predicting suitable and highly suitable areas, inspired confidence in this methodology and the resulting suitability maps. This makes it a very useful tool to target the surveillance of Ornithodoros ticks and assess the risk for Tick Borne Relapsing Fever throughout Europe

Spatial multi-criteria decision analysis for modelling suitable habitats of Ornithodoros soft ticks in the Western Palearctic region

Ticks are economically and medically important ectoparasites due to the injuries inflicted through their bite, and their ability to transmit pathogens to humans, livestock, and wildlife. Whereas hard ticks have been intensively studied, little is known about soft ticks, even though they can also transmit pathogens, including African Swine Fever Virus (ASFV) affecting domestic and wild suids or Borrelia bacteria causing tick-borne relapsing fever (TBRF) in humans. We thus developed a regional model to identify suitable spatial areas for a community of nine Ornithodoros tick species (O. erraticus, O. sonrai, O. alactagalis, O. nereensis, O. tholozani, O. papillipes, O. tartakovskyi, O. asperus, O. verrucosus), which may be of medical and veterinary importance in the Western Palearctic region. Multi-Criteria Decision Analysis was used due to the relative scarcity of high-quality occurrence data. After an in-depth literature review on the ecological requirements of the selected tick community, five climaterelated factors appeared critical for feeding activity and tick development: (i) a spring temperature exceeding 10 °C to induce the end of winter soft tick quiescent period, (ii) a three-months summer temperature above 20 °C to allow tick physiological activities, (iii) annual precipitation ranging from 60 mm to 750 mm and, in very arid areas, (iv) dry seasons interrupted by small rain showers to maintain minimum moisture inside their habitat along the year or (v) residual water provided by perennial rivers near habitats. We deliberately chose not to include biological factors such as host availability or vegetation patterns. A sensitivity analysis was done by performing multiple runs of the model altering the environmental variables, their suitability function, and their attributed weights. To validate the models, we used 355 occurrence data points, complemented by random points within sampled ecoregions. All models indicated suitable areas in the Mediterranean Basin and semidesert areas in South-West and Central Asia. Most variability between models was observed along northern and southern edges of highly suitable areas. The predictions featured a relatively good accuracy with an average Area Under Curve (AUC) of 0.779. These first models provide a useful tool for estimating the global distribution of Ornithodoros ticks and targeting their surveillance in the Western Palearctic region