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

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.

machine learning, citizen science, environmental modelling, spatio-temporal data analysis