Spatial Perspectives on Lyme Disease (Spreker: Arno Swart, RIVM)

Public health statistics recorded an increasing trend in the incidence of tick bites in the Netherlands. Tick bites may lead to the serious affliction Lyme disease.

It was 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 ticks were determined by the blanket-dragging method at numerous sites spread over the Netherlands. The probability of tick presence on a 1km by 1km square grid was estimated from the field data using a satellite-based methodology, including a wide range of environmental variables. We discuss the background, the classification algorithm, seasonality in the variables, and validation of the model output.

This, and related, work is of relevance to KNMI, since many wildlifeborne or tickborne infections are heavily driven by climate. Some further work outside of the tick-project also incorporated meteorological data from the KNMI databases, which we will briefly discuss. Currently we investigate further collaboration with KNMI in the use of climatic data (in particular the KNMI HARMONIE dataset).