Mapping tick dynamics and tick bite risk using data-driven approaches and volunteered observations

DOI

This deposit contains the materials used during the development of this PhD thesis. During this research, we applied machine learning methods to obtain new insights about tick dynamics and tick bite risk in the Netherlands. We combined volunteered data sources coming from two citizen science projects with a wide array of environmental variables (e.g. weather, remote sensing, official geodata) to devise models capable of predicting the risk of tick bite or daily tick activity at the national level. We hope that this research and the associated materials can be inspiring for future researchers.

Identifier
DOI http://dx.doi.org/doi:10.17026/dans-zre-tggd
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:131887
Provenance
Creator Garcia-Marti, I.
Publisher Data Archiving and Networked Services (DANS)
Contributor Zurita-Milla, R
Publication Year 2019
Rights info:eu-repo/semantics/restrictedAccess;DANS License
Contact Zurita-Milla, R;Faculty of Geo-Information Science and Earth Observartion ITC, Faculty of Twente
Representation
Language English
Resource Type Dataset
Coverage
Discipline Medicine
Spatial Coverage {" "," "}
Temporal Coverage Begin 2019-09-27T11:59:59Z
Temporal Coverage End 2019-08-23T11:59:59Z