Crowdsourced tick observation data was associated with diagnosed Lyme borreliosis cases with a lag of 3–4 weeks and explained variation in case numbers beyond simple temperature‐driven seasonality.Models combining tick observation data and secondary variables controlling seasonality and spatial variation were able to predict weekly Lyme borreliosis cases on a nationwide scale regardless of observation source (humans, pets or all sources).Models on smaller spatial scales functioned with only tick observation and seasonality data and revealed varying temporal patterns driven by different phenologies of local tick species.
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Title
Citizen Science Tick Observations Serve as an Early Warning System for Tick‐Borne Diseases