Predicting Tick Presence by Environmental Risk Mapping

Public health statistics recorded an increasing trend in the incidence of tick bites and erythema migrans (EM) in the Netherlands. We investigated whether the disease incidence could be predicted by a spatially explicit categorization model, based on environmental factors and a training set of tick absence–presence data. Presence and absence of Ixodes ricinus were determined by the blanket-dragging method at numerous sites spread over the Netherlands. The probability of tick presence on a 1 km by 1 km square grid was estimated from the field data using a satellite-based methodology. Expert elicitation was conducted to provide a Bayesian prior per landscape type. We applied a linear model to test for a linear relationship between incidence of EM consultations by general practitioners in the Netherlands and the estimated probability of tick presence. Ticks were present at 252 distinct sampling coordinates and absent at 425. Tick presence was estimated for 54% of the total land cover. Our model has predictive power for tick presence in the Netherlands, tick-bite incidence per municipality correlated significantly with the average probability of tick presence per grid. The estimated intercept of the linear model was positive and significant. This indicates that a significant fraction of the tick-bite consultations could be attributed to the I. ricinus population outside the resident municipality.