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Table S1. Regression coefficients for the final negative binomial GLMs for subgenus Avaritia female Culicoides collected on two breeds of sheep. Table S2. Regression coefficients for the final negative binomial GLMs for C. scoticus females collected on two breeds of sheep. Table S3. Regression coefficients for the final negative binomial GLMs for C. dewulfi females collected on two breeds of sheep. Table S4. Regression coefficients for the final negative binomial GLMs for C. chiopterus females collected on two breeds of sheep. Table S5. Regression coefficients for the final negative binomial GLMs for collections of C. obsoletus females collected on sheared and unsheared sheep. Table S6. Differences in collections between sheared and unsheared sheep and UV suction-light trap controls for C. obsoletus for total females (a), unpigmented females (b), pigmented females (c) and blood-fed females (d). Table S7. Regression coefficients for the final negative binomial GLMs for collections of C. scoticus females from sheared and unsheared sheep. Table S8. Differences in collections between sheared and unsheared sheep and UV light-suction trap controls for C. scoticus females (a), unpigmented females (b) and pigmented females (c). Table S9. Regression coefficients for the final negative binomial GLMs for collections of C. dewulfi females from sheared and unsheared sheep. Table S10. Differences in catch collections between sheared and unsheared sheep and UV light-suction trap controls for C. dewulfi females (a), unpigmented females (b) and pigmented females (c). Table S11. Regression coefficients for the final negative binomial GLMs for collections of C. chiopterus females on sheared and unsheared sheep. (DOCX 37 kb)

Increased globalisation and climate change have led to concern about the increasing risk of arthropod-borne virus (arbovirus) outbreaks globally. An outbreak of equine arboviral disease in northern Europe could impact significantly on equine welfare, and result in economic losses. Early identification of arboviral disease by horse owners may help limit disease spread. In order to determine what horse owners understand about arboviral diseases of horses and their vectors, the authors undertook an open, cross-sectional online survey of UK horse owners. The questionnaire was distributed using social media and a press release and was active between May and July 2016. There were 466 respondents, of whom 327 completed the survey in full. High proportions of respondents correctly identified photographic images of biting midges (71.2 per cent) and mosquitoes (65.4 per cent), yet few were aware that they transmit equine infectious diseases (31.4 per cent and 35.9 per cent, respectively). Of the total number of respondents, only 7.4 per cent and 16.2 per cent correctly named a disease transmitted by biting midges and mosquitoes, respectively. Only 13.1 per cent and 12.5 per cent of participants identified specific clinical signs of African horse sickness (AHS) and West Nile virus (WNV), respectively. This study demonstrates that in the event of heightened disease risk educational campaigns directed towards horse owners need to be implemented, focussing on disease awareness, clinical signs and effective disease prevention strategies.

Climate change is expected to threaten human health and well-being via its effects on climate-sensitive infectious diseases, potentially changing their spatial distributions, affecting annual/seasonal cycles, or altering disease incidence and severity. Climate sensitivity of pathogens is a key indicator that diseases might respond to climate change, but the proportion of pathogens that is climate-sensitive, and their characteristics, are not known. The climate sensitivity of European human and domestic animal infectious pathogens, and the characteristics associated with sensitivity, were assessed systematically in terms of selection of pathogens and choice of literature reviewed. Sixty-three percent (N = 157) of pathogens were climate sensitive; 82% to primary drivers such as rainfall and temperature. Protozoa and helminths, vector-borne, foodborne, soilborne and waterborne transmission routes were associated with larger numbers of climate drivers. Zoonotic pathogens were more climate sensitive than human- or animal-only pathogens. Thirty-seven percent of disability-adjusted-life-years arise from human infectious diseases that are sensitive to primary climate drivers. These results help prioritize surveillance for pathogens that may respond to climate change. Although this study identifies a high degree of climate sensitivity among important pathogens, their response to climate change will be dependent on the nature of their association with climate drivers and impacts of other drivers.

Open peer review. (PDF 357Â kb)

International audience; BACKGROUND:Plague, a zoonosis caused by Yersinia pestis, is found in Asia, the Americas but mainly in Africa, with the island of Madagascar reporting almost one third of human cases worldwide. In the highlands of Madagascar, plague is transmitted predominantly by two flea species which coexist on the island, but differ in their distribution. The endemic flea, Synopsyllus fonquerniei, dominates flea communities on rats caught outdoors, while the cosmopolitan flea, Xenopsylla cheopis, is found mostly on rats caught in houses. Additionally S. fonquerniei seems restricted to areas above 800 m. Climatic constraints on the development of the two main vectors of plague could explain the differences in their distribution and the seasonal changes in their abundance. Here we present the first study on effects of temperature and relative humidity on the immature stages of both vector species.METHODS:We examined the two species' temperature and humidity requirements under experimental conditions at five different temperatures and two relative humidities. By employing multivariate and survival analysis we established the impact of temperature and relative humidity on development times and survival for both species. Using degree-day analysis we then predicted the average developmental threshold for larvae to reach pupation and for pupae to complete development under each treatment. This analysis was undertaken separately for the two relative humidities and for the two species.RESULTS:Development times and time to death differed significantly, with the endemic S. fonquerniei taking on average 1.79 times longer to complete development and having a shorter time to death than X. cheopis under adverse conditions with high temperature and low humidity. Temperature had a significant effect on the development times of flea larvae and pupae. While humidity did not affect the development times of either species, it did influence the time of death of S. fonquerniei. Using degree-day analysis we estimated an average developmental threshold of 9 °C for S. fonquerniei, and 12.5 °C for X. cheopis.CONCLUSIONS:While many vector-borne diseases are limited to warm, low-lying regions, plague in Madagascar is unusual in being most prevalent in the cool, highland regions of the country. Our results point towards the possibility that this is because the endemic flea vector, S. fonquerniei, is better adapted to cool temperatures than the exotic flea vector, X. cheopis. Future warming caused by climate change might reduce the area suitable for S. fonquerniei and may thus reduce the incidence of plague in Madagascar.

Purpose: The Culicoides midge vectors of bluetongue (BT) are ubiquitous on farms in the UK, but little research has explored their spatial abundance, an important determinant of disease risk. Models to explain and predict variation in their abundance are needed for effective targeting of BT control methods. Although epidemiological models are commonplace at the national scale, no investigations have taken place at a finer spatial scale. Our aim is to identify determinants of midge abundance at a local 1 km scale. Methods: Midge abundances were estimated using light traps on 35 farms in north Wales. Culicoides catches were combined with remotely-sensed ecological correlates, and on-farm host and environmental data, within a general linear model. Drivers of local scale variation were determined at the 1 km resolution. Results: Local-scale variation in Obsoletus Group abundance exhibited an almost 500-fold difference (74 to 33,720) between farms, but the Obsoletus Group model explained 81% of this variance. The variance explained was consistently high for the Pulicaris Group, C. pulicaris and C. punctatus (80%, 73%, and 74%), the other possible BTV vector species in the UK. The abundance of all vector species increased with the number of sheep on farms, but this relationship was missing from any of the non-vector models. Performance of the non-vector models was also high (65-87% variance explained), but species differed in their associations with satellite variables. Conclusions: At a large spatial scale, there is significant variation in Culicoides Obsoletus Group abundance, undermining attempts to record their nationwide distribution in larger scale models, which have historically explained the abundance of these vectors poorly. Satellite data can be used to explain a high proportion of this variation and may produce effective predictive models of disease vector abundance. Relevance: This work highlights how novel local-scale modelling of disease vectors can explain a large degree of spatial variation that national-scale models fail to explain. This should be of note to policy makers when deciding upon guidelines for entomological surveys before, during and after disease outbreaks. (Texte intégral)

Background: Farm to farm movement of Culicoides midges is believed to play a critical role in the spread of bluetongue (BT), Schmallenberg and other midge-borne diseases. To help understand and predict the spread of diseases carried by midges, there is a need to determine their dispersal patterns, and to identify factors contributing to the direction taken and distance travelled. Methods: The dispersal of Obsoletus Group members was studied on 19 farms around Bala, north Wales. Field-collected Culicoides were trapped in a black-light (OVI) trap and self-marked in the collecting vessel, using micronized fluorescent dust. Culicoides were released at a central farm and OVI traps set on 18 surrounding farms, at distances of 1 to 4 km. The study was repeated using six colours of fluorescent dust over an 18 day period. Results: An estimated 61,062 (95% CI = 56,298-65,830) marked Culicoides were released during the study and 12 (0.02%) Culicoides were recaptured. Of the females recaptured, six were C. obsoletus/scoticus, two C. dewulfi, two C. pulicaris and one C. festivipennis. The male was C. obsoletus. Recaptures occurred 1–2.5 km from the release site, with greatest numbers at 2.5 km. Most recaptures were 2 nights post-release; none were more than 3 nights post-release. Two females were recovered at 1.5 km on the night of release and one male at 1 km two nights post-release. The mean distance travelled (MDT) for males was 1 km, females was 2.21 km, and all recaptured Culicoides was 2.15 km. Recaptures were made both downwind and upwind of the prevailing wind direction during the trapping periods, highlighting possible passive and active dispersal of Culicoides between farms. Conclusions: This is the first study to demonstrate farm to farm movement of the main Palaearctic BT vector species, the Obsoletus Group. Such movement has disease control implications in terms of the vectoral movement of disease between farms. The results suggest that Culicoides control measures applied at an infected farm (trapping or killing Culicoides) will reduce risk of spread to neighbouring farms by lessening the number of Culicoides dispersing from that farm, as well as reducing transmission at the source farm itself.

Bluetongue was introduced into northern Europe in 2006, and members of the Obsoletus group of Culicoides biting midges were implicated as vectors of the disease. Identification of two of the four members of this group is considered difficult, if not impossible, when undertaken morphologically. Previous studies have attempted to determine morphological techniques to differentiate these species, yet have nor investigated the effect of seasonally, or geographical location on midge morphology. Here, midges were collected from two sampling locations the UK, France and Spain. Morphometric measurements were compared between the start, middle and end of the vector. While both geographical and seasonal variation in midge morphology was identified, identification techniques, based on the morphology of the abdomen, were also determined.

International audience; Disease or pathogen risk prioritisations aid understanding of infectious agent impact within surveillance or mitigation and biosecurity work, but take significant development. Previous work has shown the H-(Hirsch-)index as an alternative proxy. We present a weighted risk analysis describing infectious pathogen impact for human health (human pathogens) and well-being (domestic animal pathogens) using an objective, evidence-based, repeatable approach; the H-index. This study established the highest H-index European pathogens. Commonalities amongst pathogens not included in previous surveillance or risk analyses were examined. Differences between host types (humans/animals/zoonotic) in pathogen H-indices were explored as a One Health impact indicator. Finally, the acceptability of the H-index proxy for animal pathogen impact was examined by comparison with other measures. 57 pathogens appeared solely in the top 100 highest H-indices (1) human or (2) animal pathogens list, and 43 occurred in both. Of human pathogens, 66 were zoonotic and 67 were emerging, compared to 67 and 57 for animals. There were statistically significant differences between H-indices for host types (humans, animal, zoonotic), and there was limited evidence that H-indices are a reasonable proxy for animal pathogen impact. This work addresses measures outlined by the European Commission to strengthen climate change resilience and biosecurity for infectious diseases. The results include a quantitative evaluation of infectious pathogen impact, and suggest greater impacts of human-only compared to zoonotic pathogens or scientific under-representation of zoonoses. The outputs separate high and low impact pathogens, and should be combined with other risk assessment methods relying on expert opinion or qualitative data for priority setting, or could be used to prioritise diseases for which formal risk assessments are not possible because of data gaps.

Culicoides midges are ubiquitous on farms in the UK, but little research has explored their abundance, an important determinant of disease risk. Models to explain and predict variation in their abundance are needed for effective targeting of control methods against bluetongue (BT) and other Culicoides-borne diseases. Midge abundances were estimated using light traps on 35 farms in north Wales. Culicoides catches were combined with remotely-sensed ecological correlates, and on-farm host and environmental data, within a general linear model. Local-scale variation in abundance exhibited an almost 500-fold difference between farms in maximum Obsoletus Group catches. The variance explained was consistently high for the BT vector species (81% for Obsoletus Group; 80% for Pulicaris Group, 73% for C. pulicaris, 74% for C. punctatus). The abundance of all vector species increased with the number of sheep on farms, but this relationship was missing from any of the non-vector models. At a large spatial scale, there is significant variation in Culicoides Obsoletus Group abundance, which undermines attempts to record their nationwide distribution in larger scale models. A prior survey should be undertaken for farms with high Culicoides catches within a sampling area and stability in catch size should be checked between seasons and years.