US-UK Collab: Eco-social dynamics of tick-borne pathogens along urbanization gradients: establishment, spillover, and management

About This Grant

Lyme disease and other diseases carried by ticks are generally linked to suburban and rural areas, but they are increasingly found in urban parks and other natural habitats in and around cities. The germs that cause these diseases are maintained by wildlife that ticks also depend on for survival, as they are their main source of blood meals. Therefore, areas with the greatest chance for humans to get sick occur where there are enough greenspaces to support wildlife and many human visitors. The project will identify these risky settings by collecting and combining wildlife, tick, and human movement information using advanced computational models. The findings of this investigation will be directly translated into disease prevention by empowering the community to improve their own health through the use of The Tick App smartphone app, a research and educational tool created by the research team. The App includes AI functions to identify ticks and provide information on risk factors and human movement; wildlife will be identified using AI identification of trail cam photos. Furthermore, the project’s outcomes equip city planners, park managers, and health officials with science-based information and practical tools to design urban green spaces that support wildlife while reducing tick encounters in urban areas. This work offers training for students and a skilled future workforce—from elementary students to postdoctoral researchers—in research methods with real-world applications to protect human health. This research project takes an integrated transdisciplinary approach to study how the risk of tick-borne diseases changes from rural to dense urban fabric in large metropolitan areas, such as New York and Boston. Wildlife and ticks will be sampled in 80+ green spaces with different levels of connectivity quantified as the total ‘current’ flowing through the landscape (Omniscape platform). Researchers use and improve AI platforms to automatically identify ticks using photos from The Tick App, wildlife species from trail camera photos, and bird species from audio recordings. Human movement patterns are assessed using GPS locations derived from The Tick App for people’s park use, in addition to self-reported tick encounters and wildlife sightings. Machine learning models, along with advanced network models, analyze and predict patterns of wildlife and tick distributions with people’s use of parks and other green spaces. The team interviews planners and land managers to understand if and how they try to reduce disease risk in park planning and management to fine-tune the Machine Learning models. The models will then simulate how changes in land use, such as adding or removing green spaces or reductions in deer populations, affect the animals that ticks feed on, and how human behavior and movement affect exposure to ticks. By building advanced computer models using real-world data and informed by manager interviews, the team aims to predict how changes in cities might increase or decrease the risk of disease, and translate the findings to improve management strategies for green spaces and wildlife to protect public health. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.