TY - JOUR
T1 - Spatial patterns of pathogen prevalence in questing Ixodes ricinus nymphs in southern Scandinavia, 2016
AU - Kjær, Lene Jung
AU - Klitgaard, Kirstine
AU - Soleng, Arnulf
AU - Edgar, Kristin Skarsfjord
AU - Lindstedt, Heidi Elisabeth H.
AU - Paulsen, Katrine M.
AU - Andreassen, Åshild Kristine
AU - Korslund, Lars
AU - Kjelland, Vivian
AU - Slettan, Audun
AU - Stuen, Snorre
AU - Kjellander, Petter
AU - Christensson, Madeleine
AU - Teräväinen, Malin
AU - Baum, Andreas
AU - Jensen, Laura Mark
AU - Bødker, Rene
PY - 2020
Y1 - 2020
N2 - 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 patternsin Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area.
AB - 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 patternsin Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area.
U2 - 10.1038/s41598-020-76334-5
DO - 10.1038/s41598-020-76334-5
M3 - Journal article
C2 - 33168841
SN - 2045-2322
VL - 10
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 19376
ER -