Potential for integrated monitoring of tick-borne diseases: Indices of tick activity, citizen science, and tick-borne Lyme neuroborreliosis in Denmark from 2017 to 2024

Monitoring programs that track natural fluctuations in tick activity, human exposure, and disease incidence are limited in their ability to detect shifts in tick-borne disease (TBD) risk. We evaluated an integrated approach combining field-based tick surveillance, Google search trends, and national Lyme neuroborreliosis (LNB) records in Denmark from 2017-2024. Tick nymph activity was modelled using meteorological data from six forest sites and validated against independent 2024-2025 data. The model showed strong predictive performance (Pearson's r = 0.76, normalised root-mean-square error = 0.16), with temperature, relative humidity, and precipitation significantly influencing activity. Predicted tick activity correlated strongly with Danish Google search terms for ticks ("Flåt" and "Tæge") with a 1-month lag, and with "borrelia" searches without lag. Predicted activity preceded LNB incidence by one month, consistent with known delays in symptom onset and diagnosis. These findings suggest that digital search behaviour may reflect early public awareness and exposure, offering potential as an early warning signal. We adopted a bottom-up modelling approach, using predicted tick activity derived from meteorological data as a shared reference to explore weather-driven congruence across field surveillance, digital search behaviour, and disease records. The strong temporal alignment across data sources supports the feasibility of integrated TBD surveillance and indicates that the six field sites provide a representative signal of tick activity and can therefore act as effective sentinel sites. Combining weather data, sentinel site activity, digital behaviour, and health records offers a scalable, cost-effective complement to traditional monitoring and may improve confidence in detected trends, enabling earlier public health responses.