C. Furlanello, M. Neteler, S. Merler,S. Menegon, S. Fontanari,
A. Donini, A. Rizzoli, C. Chemini
GIS and the Random Forest Predictor: Integration in R for
Tick-Borne Disease Risk Assessment
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We discuss how sophisticated machine learning methods may be rapidly
integrated within a GIS for the development of new approaches in
landscape epidemiology. A multitemporal predictive map is obtained
by modeling in R, analyzing geodata and digital maps in GRASS,
and managing biodata samples and weather data in PostgreSQL. In
particular, we present a risk mapping system for tick-borne
diseases, applied to model the risk of exposure to Lyme borreliosis
and tick-borne encephalitis (TBE) in Trentino, Italian Alps.