Cesare Furlanello, Stefano Merler, Annapaola Rizzoli*, Claudio Chemini*, Claudio Genchi**.
ITC-IRST, Trento, *Centro di Ecologia Alpina, Trento, **Institute of General Pathology and Parassitology, Veterinary Medicine, MilanoUniversity, Italy
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Risk assessment of exposure to Lyme borreliosis,
a tick-borne illness common in North America and endemic in several regions of Europe, is
crucial for prevention, the prompt diagnosis of disease and the treatment of cardiac
complications (Lyme carditis). In regions where the epidemic is prevalent, the presence of
Borrelia burgdoferi (the agent of Lyme disease) could, in fact, be suspected in otherwise
healthy patients with unexplained cardiac symptoms such as supraventricular tachiarrhythmia,
atrioventricular block of unknown origin, and in dilated cardiomiopathy. The construction of
predictive geospatial risk models based on critical ecological factors and rates of infection in
ticks is thus proposed for a landscape epidemiology of Lyme disease. A computational
method based on bootstrap aggregation (bagging) of tree-based classifiers integrated with
a Geographical Information System (GIS) has been developed to predict the risk of Lyme
disease in province of Trento. The risk of bite exposure was estimated from 438 field
samples for a total of 3422 specimens of Ixodes ricinus. One hundred risk classification
models were built by bootstrap resampling of a data base associating tick sampling to the
variables extracted from GIS thematic digital maps of elevation, the type of vegetation and
soil, exposure, and the density of roe deer (a key host for adult ticks). The final model was
then obtained via the aggregation of the 100 sub-models and tested for predictivity also in
comparison to previous results obtained with a single predictive classification tree (b632+
model) and with a classical logistic regression model developed on the same data set.
Additional data sets and measures of prevalence of infection were then used to cross-validate
and further extend the model. A tree-based model is now available that predicts local risk of
tick presence over cells of size 50 x 50 meters. The overall accuracy of the bagging based
model is 75% with respect to the positive samples (presence of ticks) and 76% with respect
to the negative ones. The model stabilizes the previous single tree model prediction. A
classical linear model based on stepwise variable selection exhibited poorer performance
(av. accuracy: 65±5%). On an additional data base, high infestation was correctly predicted
by bagging in 81% of cases. Bagging has therefore improved the computational model for
risk assessment of tick bites in Trentino. The resulting GIS based model is currently being
expanded in order to directly predict the risk of B. burgdoferi infection for the purpose of
reducing human exposure to Lyme disease and, thus, facilitate the recognition and the
correct treatment of idiopathic heart failure.
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