People have good reasons for being afraid of ticks. They are currently on the rise and transmit several infectious diseases, leading to serious illness or even death. In 2018, cases of recorded tick-borne infectious diseases continued to rise, and so did the fear for tick bites. Since March 2015, ZHAW and its spin-off A&K Strategy are operating the smartphone application “Zecke–Tick Prevention”. This app helps people who have been bitten by a tick to remember the tick bite location and to check it for potential Lyme disease symptoms. Half of all tick bites are not noticed and very often, it is not clear at which geographic position and when exactly a tick has bitten someone. The tick incidences reported in the app can serve as a basis for scientists to learn more about spatial tick risks.
ZHAW News "Nur jeder Vierte führt Symptomkontrolle nach Zeckenstich durch"
Working with anonymously transmitted data of unknown quality is the biggest challenge for this study:
/ We want to promote public health with a crowdsourced tick prevention tool to visualize the special tick risk dynamically.
/ We want to develop a method to handle the notoriously uncertain crowdsourced Citizen Science data.
/ We want to create a model to serve people with spatial information about the current tick risk.
Up-to-date visualizations of the tick bite risk are intended to help reducing the risk of tick-borne diseases and, for example, can support teachers in planning locations for forest excursions. An interdisciplinary approach is envisaged that will apply geographical information system scientists (IUNR Geoinformatics group), artificial intelligence and data scientists (IAS Bio-Inspired Modeling & Learning Systems group) and experts in tick biology and prevention (IUNR Phytomedicine and Environmental Genomics and Systems Biology groups). The newly developed model and methods will evoke the interest of the broad public due to its Citizen Science aspect and by addressing an emotionally charged health topic that has become everyone’s concern.
IUNR Geoinformatics group
IAS Bio-Inspired Modeling & Learning Systems group