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Predicting tick presence by environmental risk mapping.

Arno Swart1, Adolfo Ibañez-Justicia2, Jan Buijs3

  • 1Centre for Infectious Disease Control, National Institute for Public Health and the Environment , Bilthoven , Netherlands.

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Summary
This summary is machine-generated.

Public health data show more tick bites and erythema migrans (EM) in the Netherlands. A new model predicts tick presence using environmental data, helping understand disease incidence and geographic risk factors.

Keywords:
Borrelialymerisk mappingticks

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Area of Science:

  • Epidemiology
  • Environmental Science
  • Veterinary Entomology

Background:

  • Public health statistics indicate a rising trend in tick bites and erythema migrans (EM) cases in the Netherlands.
  • Understanding the spatial distribution and incidence of tick-borne diseases is crucial for effective public health interventions.

Purpose of the Study:

  • To develop and validate a spatially explicit model for predicting the presence of Ixodes ricinus ticks.
  • To assess the relationship between predicted tick presence and the incidence of EM consultations in the Netherlands.

Main Methods:

  • Field data on tick presence/absence were collected using the blanket-dragging method across the Netherlands.
  • A satellite-based methodology estimated tick presence probability on a 1km x 1km grid, incorporating Bayesian priors from expert elicitation.
  • A linear model was used to correlate EM consultation incidence with estimated tick presence probability.

Main Results:

  • Ixodes ricinus ticks were found at 252 sampling sites and absent at 425.
  • The model successfully estimated tick presence for 54% of the total land cover, demonstrating predictive power.
  • Tick-bite incidence per municipality significantly correlated with the average probability of tick presence, with a significant positive intercept.

Conclusions:

  • The developed model effectively predicts tick presence in the Netherlands.
  • The study highlights that a substantial portion of EM consultations may be linked to tick populations outside a patient's immediate municipality of residence.
  • Spatially explicit modeling is a valuable tool for understanding and managing tick-borne disease risk.