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Ixodes ricinus ticks are Scandinavia's main vector for tick-borne encephalitis virus (TBEV), which infects many people annually. The aims of the present study were (i) to obtain information on the TBEV prevalence in host-seeking I. ricinus collected within the Øresund-Kattegat-Skagerrak (ØKS) region, which lies in southern Norway, southern Sweden and Denmark; (ii) to analyse whether there are potential spatial patterns in the TBEV prevalence; and (iii) to understand the relationship between TBEV prevalence and meteorological factors in southern Scandinavia. Tick nymphs were collected in 2016, in southern Scandinavia, and screened for TBEV, using pools of 10 nymphs, with RT real-time PCR, and positive samples were confirmed with pyrosequencing. Spatial autocorrelation and cluster analysis was performed with Global Moran's I and SatScan to test for spatial patterns and potential local clusters of the TBEV pool prevalence at each of the 50 sites. A climatic analysis was made to correlate parameters such as minimum, mean and maximum temperature, relative humidity and saturation deficit with TBEV pool prevalence. The climatic data were acquired from the nearest meteorological stations for 2015 and 2016. This study confirms the presence of TBEV in 12 out of 30 locations in Denmark, where six were from Jutland, three from Zealand and two from Bornholm and Falster counties. In total, five out of nine sites were positive from southern Sweden. TBEV prevalence of 0.7%, 0.5% and 0.5%, in nymphs, was found at three sites along the Oslofjord (two sites) and northern Skåne region (one site), indicating a potential concern for public health. We report an overall estimated TBEV prevalence of 0.1% in questing I. ricinus nymphs in southern Scandinavia with a region-specific prevalence of 0.1% in Denmark, 0.2% in southern Sweden and 0.1% in southeastern Norway. No evidence of a spatial pattern or local clusters was found in the study region. We found a strong correlation between TBE

Ixodes ricinus ticks are Scandinavia's main vector for tick-borne encephalitis virus (TBEV), which infects many people annually. The aims of the present study were (i) to obtain information on the TBEV prevalence in host-seeking I. ricinus collected within the Øresund-Kattegat-Skagerrak (ØKS) region, which lies in southern Norway, southern Sweden and Denmark; (ii) to analyse whether there are potential spatial patterns in the TBEV prevalence; and (iii) to understand the relationship between TBEV prevalence and meteorological factors in southern Scandinavia. Tick nymphs were collected in 2016, in southern Scandinavia, and screened for TBEV, using pools of 10 nymphs, with RT real-time PCR, and positive samples were confirmed with pyrosequencing. Spatial autocorrelation and cluster analysis was performed with Global Moran's I and SatScan to test for spatial patterns and potential local clusters of the TBEV pool prevalence at each of the 50 sites. A climatic analysis was made to correlate parameters such as minimum, mean and maximum temperature, relative humidity and saturation deficit with TBEV pool prevalence. The climatic data were acquired from the nearest meteorological stations for 2015 and 2016. This study confirms the presence of TBEV in 12 out of 30 locations in Denmark, where six were from Jutland, three from Zealand and two from Bornholm and Falster counties. In total, five out of nine sites were positive from southern Sweden. TBEV prevalence of 0.7%, 0.5% and 0.5%, in nymphs, was found at three sites along the Oslofjord (two sites) and northern Skåne region (one site), indicating a potential concern for public health. We report an overall estimated TBEV prevalence of 0.1% in questing I. ricinus nymphs in southern Scandinavia with a region-specific prevalence of 0.1% in Denmark, 0.2% in southern Sweden and 0.1% in southeastern Norway. No evidence of a spatial pattern or local clusters was found in the study region. We found a strong correlation between TBE

Ixodes ricinus ticks are Scandinavia's main vector for tick-borne encephalitis virus (TBEV), which infects many people annually. The aims of the present study were (i) to obtain information on the TBEV prevalence in host-seeking I. ricinus collected within the Øresund-Kattegat-Skagerrak (ØKS) region, which lies in southern Norway, southern Sweden and Denmark; (ii) to analyse whether there are potential spatial patterns in the TBEV prevalence; and (iii) to understand the relationship between TBEV prevalence and meteorological factors in southern Scandinavia. Tick nymphs were collected in 2016, in southern Scandinavia, and screened for TBEV, using pools of 10 nymphs, with RT real-time PCR, and positive samples were confirmed with pyrosequencing. Spatial autocorrelation and cluster analysis was performed with Global Moran's I and SatScan to test for spatial patterns and potential local clusters of the TBEV pool prevalence at each of the 50 sites. A climatic analysis was made to correlate parameters such as minimum, mean and maximum temperature, relative humidity and saturation deficit with TBEV pool prevalence. The climatic data were acquired from the nearest meteorological stations for 2015 and 2016. This study confirms the presence of TBEV in 12 out of 30 locations in Denmark, where six were from Jutland, three from Zealand and two from Bornholm and Falster counties. In total, five out of nine sites were positive from southern Sweden. TBEV prevalence of 0.7%, 0.5% and 0.5%, in nymphs, was found at three sites along the Oslofjord (two sites) and northern Skåne region (one site), indicating a potential concern for public health. We report an overall estimated TBEV prevalence of 0.1% in questing I. ricinus nymphs in southern Scandinavia with a region-specific prevalence of 0.1% in Denmark, 0.2% in southern Sweden and 0.1% in southeastern Norway. No evidence of a spatial pattern or local clusters was found in the study region. We found a strong correlation between TBE

Tick-borne pathogens cause diseases in animals and humans, and tick-borne disease incidence is increasing in many parts of the world. There is a need to assess the distribution of tick-borne pathogens and identify potential risk areas. We collected 29,440 tick nymphs from 50 sites in Scandinavia from August to September, 2016. We tested ticks in a real-time PCR chip, screening for 19 vector-associated pathogens. We analysed spatial patterns, mapped the prevalence of each pathogen and used machine learning algorithms and environmental variables to develop predictive prevalence models. All 50 sites had a pool prevalence of at least 33% for one or more pathogens, the most prevalent being Borrelia afzelii, B. garinii, Rickettsia helvetica, Anaplasma phagocytophilum, and Neoehrlichia mikurensis. There were large diferences in pathogen prevalence between sites, but we identifed only limited geographical clustering. The prevalence models performed poorly, with only models for R. helvetica and N. mikurensis having moderate predictive power (normalized RMSE from 0.74–0.75, R2 from 0.43–0.48). The poor performance of the majority of our prevalence models suggest that the used environmental and climatic variables alone do not explain pathogen prevalence patterns in Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area.

Ticks carry pathogens that can cause disease in both animals and humans, and there is a need to monitor the distribution and abundance of ticks and the pathogens they carry to pinpoint potential high risk areas for tick-borne disease transmission. In a joint Scandinavian study, we measured Ixodes ricinus instar abundance at 159 sites in southern Scandinavia in August-September, 2016, and collected 29,440 tick nymphs at 50 of these sites. We additionally measured abundance at 30 sites in August-September, 2017. We tested the 29,440 tick nymphs in pools of 10 in a Fluidigm real-time PCR chip to screen for 17 different tick-associated pathogens, 2 pathogen groups and 3 tick species. We present data on the geolocation, habitat type and instar abundance of the surveyed sites, as well as presence/absence of each pathogen in all analysed pools from the 50 collection sites and individual prevalence for each site. These data can be used alone or in combination with other data for predictive modelling and mapping of high-risk areas.

Tick-borne pathogens cause diseases in animals and humans, and tick-borne disease incidence is increasing in many parts of the world. There is a need to assess the distribution of tick-borne pathogens and identify potential risk areas. We collected 29,440 tick nymphs from 50 sites in Scandinavia from August to September, 2016. We tested ticks in a real-time PCR chip, screening for 19 vector-associated pathogens. We analysed spatial patterns, mapped the prevalence of each pathogen and used machine learning algorithms and environmental variables to develop predictive prevalence models. All 50 sites had a pool prevalence of at least 33% for one or more pathogens, the most prevalent being Borrelia afzelii, B. garinii, Rickettsia helvetica, Anaplasma phagocytophilum, and Neoehrlichia mikurensis. There were large differences in pathogen prevalence between sites, but we identified only limited geographical clustering. The prevalence models performed poorly, with only models for R. helvetica and N. mikurensis having moderate predictive power (normalized RMSE from 0.74–0.75, R2 from 0.43–0.48). The poor performance of the majority of our prevalence models suggest that the used environmental and climatic variables alone do not explain pathogen prevalence patterns in Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area., Tick-borne pathogens cause diseases in animals and humans, and tick-borne disease incidence is increasing in many parts of the world. There is a need to assess the distribution of tick-borne pathogens and identify potential risk areas. We collected 29,440 tick nymphs from 50 sites in Scandinavia from August to September, 2016. We tested ticks in a real-time PCR chip, screening for 19 vector-associated pathogens. We analysed spatial patterns, mapped the prevalence of each pathogen and used machine learning algorithms and environmental variables to develop predictive prevalence models. All 50 sites had a pool prevalence of at least 33% for one or more pathogens, the most prevalent being Borrelia afzelii, B. garinii, Rickettsia helvetica, Anaplasma phagocytophilum, and Neoehrlichia mikurensis. There were large differences in pathogen prevalence between sites, but we identified only limited geographical clustering. The prevalence models performed poorly, with only models for R. helvetica and N. mikurensis having moderate predictive power (normalized RMSE from 0.74–0.75, R2 from 0.43–0.48). The poor performance of the majority of our prevalence models suggest that the used environmental and climatic variables alone do not explain pathogen prevalence patterns in Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area.

Tick-borne pathogens cause diseases in animals and humans, and tick-borne disease incidence is increasing in many parts of the world. There is a need to assess the distribution of tick-borne pathogens and identify potential risk areas. We collected 29,440 tick nymphs from 50 sites in Scandinavia from August to September, 2016. We tested ticks in a real-time PCR chip, screening for 19 vector-associated pathogens. We analysed spatial patterns, mapped the prevalence of each pathogen and used machine learning algorithms and environmental variables to develop predictive prevalence models. All 50 sites had a pool prevalence of at least 33% for one or more pathogens, the most prevalent being Borrelia afzelii, B. garinii, Rickettsia helvetica, Anaplasma phagocytophilum, and Neoehrlichia mikurensis. There were large differences in pathogen prevalence between sites, but we identified only limited geographical clustering. The prevalence models performed poorly, with only models for R. helvetica and N. mikurensis having moderate predictive power (normalized RMSE from 0.74–0.75, R2 from 0.43–0.48). The poor performance of the majority of our prevalence models suggest that the used environmental and climatic variables alone do not explain pathogen prevalence patterns in Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area., Tick-borne pathogens cause diseases in animals and humans, and tick-borne disease incidence is increasing in many parts of the world. There is a need to assess the distribution of tick-borne pathogens and identify potential risk areas. We collected 29,440 tick nymphs from 50 sites in Scandinavia from August to September, 2016. We tested ticks in a real-time PCR chip, screening for 19 vector-associated pathogens. We analysed spatial patterns, mapped the prevalence of each pathogen and used machine learning algorithms and environmental variables to develop predictive prevalence models. All 50 sites had a pool prevalence of at least 33% for one or more pathogens, the most prevalent being Borrelia afzelii, B. garinii, Rickettsia helvetica, Anaplasma phagocytophilum, and Neoehrlichia mikurensis. There were large differences in pathogen prevalence between sites, but we identified only limited geographical clustering. The prevalence models performed poorly, with only models for R. helvetica and N. mikurensis having moderate predictive power (normalized RMSE from 0.74–0.75, R2 from 0.43–0.48). The poor performance of the majority of our prevalence models suggest that the used environmental and climatic variables alone do not explain pathogen prevalence patterns in Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area.

Recently, focus on tick-borne diseases has increased as ticks and their pathogens have become widespread and represent a health problem in Europe. Understanding the epidemiology of tick-borne infections requires the ability to predict and map tick abundance. We measured Ixodes ricinus abundance at 159 sites in southern Scandinavia from August-September, 2016. We used field data and environmental variables to develop predictive abundance models using machine learning algorithms, and also tested these models on 2017 data. Larva and nymph abundance models had relatively high predictive power (normalized RMSE from 0.65–0.69, R2 from 0.52–0.58) whereas adult tick models performed poorly (normalized RMSE from 0.94–0.96, R2 from 0.04–0.10). Testing the models on 2017 data produced good results with normalized RMSE values from 0.59–1.13 and R2 from 0.18–0.69. The resulting 2016 maps corresponded well with known tick abundance and distribution in Scandinavia. The models were highly influenced by temperature and vegetation, indicating that climate may be an important driver of I. ricinus distribution and abundance in Scandinavia. Despite varying results, the models predicted abundance in 2017 with high accuracy. The models are a first step towards environmentally driven tick abundance models that can assist in determining risk areas and interpreting human incidence data.

Recently, focus on tick-borne diseases has increased as ticks and their pathogens have become widespread and represent a health problem in Europe. Understanding the epidemiology of tick-borne infections requires the ability to predict and map tick abundance. We measured Ixodes ricinus abundance at 159 sites in southern Scandinavia from August-September, 2016. We used field data and environmental variables to develop predictive abundance models using machine learning algorithms, and also tested these models on 2017 data. Larva and nymph abundance models had relatively high predictive power (normalized RMSE from 0.65–0.69, R2 from 0.52–0.58) whereas adult tick models performed poorly (normalized RMSE from 0.94–0.96, R2 from 0.04–0.10). Testing the models on 2017 data produced good results with normalized RMSE values from 0.59–1.13 and R2 from 0.18–0.69. The resulting 2016 maps corresponded well with known tick abundance and distribution in Scandinavia. The models were highly influenced by temperature and vegetation, indicating that climate may be an important driver of I. ricinus distribution and abundance in Scandinavia. Despite varying results, the models predicted abundance in 2017 with high accuracy. The models are a first step towards environmentally driven tick abundance models that can assist in determining risk areas and interpreting human incidence data.

The taiga tick, Ixodes persulcatus, has previously been limited to eastern Europe and northern Asia, but recently its range has expanded to Finland and northern Sweden. The species is of medical importance, as it, along with a string of other pathogens, may carry the Siberian and Far Eastern subtypes of tick-borne encephalitis virus. These subtypes appear to cause more severe disease, with higher fatality rates than the central European subtype. Until recently, the meadow tick, Dermacentor reticulatus, has been absent from Scandinavia, but has now been detected in Denmark, Norway and Sweden. Dermacentor reticulatus carries, along with other pathogens, Babesia canis and Rickettsia raoultii. Babesia canis causes severe and often fatal canine babesiosis, and R. raoultii may cause disease in humans. We collected 600 tick nymphs from each of 50 randomly selected sites in Denmark, southern Norway and south-eastern Sweden in August-September 2016. We tested pools of 10 nymphs in a Fluidigm real time PCR chip to screen for I. persulcatus and D. reticulatus, as well as tick-borne pathogens. Of all the 30,000 nymphs tested, none were I. persulcatus or D. reticulatus. Our results suggest that I. persulcatus is still limited to the northern parts of Sweden, and have not expanded into southern parts of Scandinavia. According to literature reports and supported by our screening results, D. reticulatus may yet only be an occasional guest in Scandinavia without established populations.

Recently, focus on tick-borne diseases has increased as ticks and their pathogens have become widespread and represent a health problem in Europe. Understanding the epidemiology of tick-borne infections requires the ability to predict and map tick abundance. We measured Ixodes ricinus abundance at 159 sites in southern Scandinavia from August-September, 2016. We used field data and environmental variables to develop predictive abundance models using machine learning algorithms, and also tested these models on 2017 data. Larva and nymph abundance models had relatively high predictive power (normalized RMSE from 0.65–0.69, R2 from 0.52–0.58) whereas adult tick models performed poorly (normalized RMSE from 0.94–0.96, R2 from 0.04–0.10). Testing the models on 2017 data produced good results with normalized RMSE values from 0.59–1.13 and R2 from 0.18–0.69. The resulting 2016 maps corresponded well with known tick abundance and distribution in Scandinavia. The models were highly influenced by temperature and vegetation, indicating that climate may be an important driver of I. ricinus distribution and abundance in Scandinavia. Despite varying results, the models predicted abundance in 2017 with high accuracy. The models are a first step towards environmentally driven tick abundance models that can assist in determining risk areas and interpreting human incidence data.

Recently, focus on tick-borne diseases has increased as ticks and their pathogens have become widespread and represent a health problem in Europe. Understanding the epidemiology of tickborne infections requires the ability to predict and map tick abundance. We measured Ixodes ricinus abundance at 159 sites in southern Scandinavia from August-September, 2016. We used feld data and environmental variables to develop predictive abundance models using machine learning algorithms, and also tested these models on 2017 data. Larva and nymph abundance models had relatively high predictive power (normalized RMSE from 0.65–0.69, R2 from 0.52–0.58) whereas adult tick models performed poorly (normalized RMSE from 0.94–0.96, R2 from 0.04–0.10). Testing the models on 2017 data produced good results with normalized RMSE values from 0.59–1.13 and R2 from 0.18–0.69. The resulting 2016 maps corresponded well with known tick abundance and distribution in Scandinavia. The models were highly infuenced by temperature and vegetation, indicating that climate may be an important driver of I. ricinus distribution and abundance in Scandinavia. Despite varying results, the models predicted abundance in 2017 with high accuracy. The models are a frst step towards environmentally driven tick abundance models that can assist in determining risk areas and interpreting human incidence data.

The taiga tick, Ixodes persulcatus, has previously been limited to eastern Europe and northern Asia, but recently its range has expanded to Finland and northern Sweden. The species is of medical importance, as it, along with a string of other pathogens, may carry the Siberian and Far Eastern subtypes of tick-borne encephalitis virus. These subtypes appear to cause more severe disease, with higher fatality rates than the central European subtype. Until recently, the meadow tick, Dermacentor reticulatus, has been absent from Scandinavia, but has now been detected in Denmark, Norway and Sweden. Dermacentor reticulatus carries, along with other pathogens, Babesia canis and Rickettsia raoultii. Babesia canis causes severe and often fatal canine babesiosis, and R. raoultii may cause disease in humans. We collected 600 tick nymphs from each of 50 randomly selected sites in Denmark, southern Norway and south-eastern Sweden in August-September 2016. We tested pools of 10 nymphs in a Fluidigm real time PCR chip to screen for I. persulcatus and D. reticulatus, as well as tick-borne pathogens. Of all the 30,000 nymphs tested, none were I. persulcatus or D. reticulatus. Our results suggest that I. persulcatus is still limited to the northern parts of Sweden, and have not expanded into southern parts of Scandinavia. According to literature reports and supported by our screening results, D. reticulatus may yet only be an occasional guest in Scandinavia without established populations.

The taiga tick, Ixodes persulcatus, has previously been limited to eastern Europe and northern Asia, but recently its range has expanded to Finland and northern Sweden. The species is of medical importance, as it, along with a string of other pathogens, may carry the Siberian and Far Eastern subtypes of tick-borne encephalitis virus. These subtypes appear to cause more severe disease, with higher fatality rates than the central European subtype. Until recently, the meadow tick, Dermacentor reticulatus, has been absent from Scandinavia, but has now been detected in Denmark, Norway and Sweden. Dermacentor reticulatus carries, along with other pathogens, Babesia canis and Rickettsia raoultii. Babesia canis causes severe and often fatal canine babesiosis, and R. raoultii may cause disease in humans. We collected 600 tick nymphs from each of 50 randomly selected sites in Denmark, southern Norway and south-eastern Sweden in August-September 2016. We tested pools of 10 nymphs in a Fluidigm real time PCR chip to screen for I. persulcatus and D. reticulatus, as well as tick-borne pathogens. Of all the 30,000 nymphs tested, none were I. persulcatus or D. reticulatus. Our results suggest that I. persulcatus is still limited to the northern parts of Sweden, and have not expanded into southern parts of Scandinavia. According to literature reports and supported by our screening results, D. reticulatus may yet only be an occasional guest in Scandinavia without established populations.

Background: Tick-borne diseases have become increasingly common in recent decades and present a health problem in many parts of Europe. Control and prevention of these diseases require a better understanding of vector distribution. Aim: Our aim was to create a model able to predict the distribution of Ixodes ricinus nymphs in southern Scandinavia and to assess how this relates to risk of human exposure. Methods: We measured the presence of I. ricinus tick nymphs at 159 stratified random lowland forest and meadow sites in Denmark, Norway and Sweden by dragging 400 m transects from August to September 2016, representing a total distance of 63.6 km. Using climate and remote sensing environmental data and boosted regression tree modelling, we predicted the overall spatial distribution of I. ricinus nymphs in Scandinavia. To assess the potential public health impact, we combined the predicted tick distribution with human density maps to determine the proportion of people at risk. Results: Our model predicted the spatial distribution of I. ricinus nymphs with a sensitivity of 91% and a specificity of 60%. Temperature was one of the main drivers in the model followed by vegetation cover. Nymphs were restricted to only 17.5% of the modelled area but, respectively, 73.5%, 67.1% and 78.8% of the human populations lived within 5 km of these areas in Denmark, Norway and Sweden. Conclusion: The model suggests that increasing temperatures in the future may expand tick distribution geographically in northern Europe, but this may only affect a small additional proportion of the human population.

In recent years, focus on tick-borne diseases has increased as diseases such as Lyme disease and tick-borne encephalitis have become more common and represent a health problem in many parts of Scandinavia. More effective prevention of infections requires a better understanding of the factors affecting the vector abundance as well as human exposure to the vectors. Hence, there is a great need for analyses and models that can predict how vectors and their associated diseases are distributed now and possibly in the future. As a part of the ScandTick Innovation project, we surveyed tick nymphs at 159 sites (forests and meadows) in Denmark, southern Norway and south-eastern Sweden. At each site we measured presence/absence, and used the data obtained along with environmental data from satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution in southern Scandinavia. Together with the predicted distribution maps, we used human density maps to identify and plot areas with high risk of exposure to ticks. The predicted distribution and the spatial variation found corresponded well with known distributions of ticks in Scandinavia (sensitivity: 91%, specificity: 60%), and we found that the model was predominantly temperature-driven. Because presence was strongly correlated with forested habitats the risk areas were much larger in Sweden and Norway compared to Denmark. When combining these distribution maps with human population density maps, we were able to quantify the proportion of people living in areas with tick presence in Scandinavia. We found that although tick nymphs were restricted to a small proportion of the modelled area, high proportions of the human populations (67-79%) lived within these same areas. The model suggests that a potential future range expansion of I. ricinus in Scandinavia is likely but may only affect a relatively small additional proportion of the human population.