In 2016, we developed a model capable of predicting daily tick activity in Dutch forests. This model used observations collected by a citizen science initiative: a small group of dedicated volunteers monitored ticks once a month for over a decade, creating one of the most unique and long-term datasets of the field.
Well, another PhD journey comes to an end. These have been fruitful and intense years of research and fast-paced learning. The PhD research is one of the few occasions in one’s professional career in which you have the opportunity of giving full dedication to study a topic in depth for several years.
The fourth paper is currently under review for PLOS ONE, but you can read the pre-print in bioRxiv. In this work we basically take all the knowledge gained and the building blocks developed in the three previous publications to create a tick bite risk model.
The third paper associated with this research was published in Scientific Reports in 2018. Let’s first recap the two previous publications. In the first one we learnt that the volunteered tick bites collection represents the risk (R) of getting a tick bite.
The second paper of my research was published in 2017 in the International Journal of Health Geographics. In this article we explain what is our approach to model tick activity, a proxy for tick hazard (H).
The first paper was published in 2016 in a special issue of Transactions in GIS on the Role of Volunteered Geographic Information in Advancing Science. A year before, a very enthusiastic and naïve self had received a collection of 35,000 volunteered tick bites provided by the RIVM.