Search

Incidence of tick-borne encephalitis (TBE) has increased during the last years in Scandinavia, but the underlying mechanism is not understood. TBE human case data reported between 2010 and 2021 were aggregated into postal codes within Örebro County, south-central Sweden, along with tick abundance and environmental data to analyse spatial patterns and identify drivers of TBE. We identified a substantial and continuing increase of TBE incidence in Örebro County during the study period. Spatial cluster analyses showed significant hotspots (higher number of cases than expected) in the southern and northern parts of Örebro County, whereas a cold spot (lower number of cases than expected) was found in the central part comprising Örebro municipality. Generalised linear models showed that the risk of acquiring TBE increased by 12.5% and 72.3% for every percent increase in relative humidity and proportion of wetland forest, respectively, whereas the risk decreased by 52.8% for every degree Celsius increase in annual temperature range. However, models had relatively low goodness of fit (R 2  < 0.27). Results suggest that TBE in Örebro County is spatially clustered, however variables used in this study, i.e., climatic variables, forest cover, water, tick abundance, sheep as indicator species, alone do not explain this pattern., (© 2023. The Author(s).)

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, R 2 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.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

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, R 2 from 0.52-0.58) whereas adult tick models performed poorly (normalized RMSE from 0.94-0.96, R 2 from 0.04-0.10). Testing the models on 2017 data produced good results with normalized RMSE values from 0.59-1.13 and R 2 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.