Towards improved, cost-effective surveillance of Ixodes ricinus ticks and associated pathogens using species distribution modelling

Submitted: 16 October 2018
Accepted: 28 November 2018
Published: 13 May 2019
Abstract Views: 1576
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Various ticks exist in the temperate hilly and pre-alpine areas of Northern Italy, where Ixodes ricinus is the more important. In this area different tick-borne pathogen monitoring projects have recently been implemented; we present here the results of a twoyear field survey of ticks and associated pathogens, conducted 2009-2010 in North-eastern Italy. The cost-effectiveness of different sampling strategies, hypothesized a posteriori based on two sub-sets of data, were compared and analysed. The same two subsets were also used to develop models of habitat suitability, using a maximum entropy algorithm based on remotely sensed data. Comparison of the two strategies (in terms of number of ticks collected, rates of pathogen detection and model accuracy) indicated that monitoring at many temporary sites was more cost-effective than monthly samplings at a few permanent sites. The two model predictions were similar and provided a greater understanding of ecological requirements of I. ricinus in the study area. Dense vegetation cover, as measured by the normalized difference vegetation index, was identified as a good predictor of tick presence, whereas high summer temperatures appeared to be a limiting factor. The study suggests that it is possible to obtain realistic results (in terms of pathogens detection and development of habitat suitability maps) with a relatively limited sampling effort and a wellplanned monitoring strategy.

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Supporting Agencies

Højgaards Foundation;, Danish National Research Foundation, University of Padova

How to Cite

Signorini, M., Stensgaard, A.-S., Drigo, M., Simonato, G., Marcer, F., Montarsi, F., Martini, M., & Cassini, R. (2019). Towards improved, cost-effective surveillance of Ixodes ricinus ticks and associated pathogens using species distribution modelling. Geospatial Health, 14(1). https://doi.org/10.4081/gh.2019.745

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