Background: Recently, there has been an increased focus on tick-borne diseases, especially tick-borne encephalitis and Lyme borreliosis (LB) with neuroborreliosis (NB, the most severe form of LB), driven by the growing prevalence of ticks and their pathogens in various parts of Europe. This surge poses a significant health challenge. Effectively preventing tick bites and the associated infections necessitates a deeper understanding of the factors influencing tick abundance and activity patterns.

Methods: As part of the Danish national vector surveillance program, we continuously survey Ixodes ricinus abundance at 6 forest sites in Northern Zealand, Denmark. At each site, we measure the abundance of larvae, nymphs and adult ticks collected from vegetation during 10 minutes, providing a measure of abundance comparable between sites and over time. The temporal aspects also allow us to investigate the temporal activity dynamics of I. ricinus in Denmark. We used collected data from June 19th, 2017 to January 4th, 2024 on I. ricinus nymphs along with meteorological variables obtained from the Danish Meteorological Institute to develop generalized linear mixed models on tick nymph abundance/activity in Denmark, accounting for correlated meteorological variables, a temporal trend, temporal autocorrelation and random variations between sites and years. We then compared model results with monthly incidence data on human NB in Denmark obtained from Statens Serum Institut to investigate potential overlaps in the temporal patterns.

Results: All 6 sites showed similar temporal patterns in nymph activity, with low to no activity during the winter months. Preliminary results show that meteorological factors related to temperature, humidity, amount of sunshine, and atmospheric pressure are associated with tick nymph abundance/activity (P<0.05). When using leave-one-out cross validation, the model showed a high correlation between observed and predicted values (r=0.67, P<0.0001, normalized mean square error =0.67). Comparing the predicted monthly average tick abundance/activity to human disease incidence data of NB for the years 2017 - 2023, showed overlap with the timing of high tick activity and high NB incidence albeit with a slight lag in the NB data. A cross-correlation analysis showed the highest correlation coefficient r= 0.86 (P<0.001) at lag -1 month, with increasing/decreasing tick nymph numbers happening a month before decreasing/increasing number of NB cases. However, as the data used for this comparison were aggregated by month, this lag could span anywhere from 2-6 weeks.

Conclusion: The generated model aligned closely with established activity patterns of ticks in Northern Europe. The model exhibited a strong association with meteorological factors underlining the significance of climate as a key factor influencing the abundance and activity patterns of I. ricinus in Northern Europe. The temporal patterns of tick activity in the model aligned with temporal patterns of human NB in Denmark indicating that this model may be valuable for incorporation into epidemiological models, aiding in the identification of temporal risk periods and the interpretation of human disease incidence data.