Repeated holdout cross-validation of model to estimate risk of Lyme disease by landscape characteristics

摘要

We previously modeled Lyme disease (LD) risk at the landscape scale; here we evaluate the model's overall goodness-of-fit using holdout validation. Landscapes were characterized within road-bounded analysis units (AU). Observed LD cases (obsLD) were ascertained per AU. Data were randomly subset 2,000 times. Of 514 AU, 411 (80%) were selected as a training dataset to develop parameter estimates used to predict observations in the remaining 103 (20%) AU, the validation subset. Predicted values were subtracted from obsLD to quantify accuracy across iterations. We calculated the percentage difference of over- and under-estimation to assess bias. Predictive ability was strong and similar across iterations and datasets; the exact number of obsLD cases per AU were predicted almost 60% of the time. However, the three highest obsLD AU were under-predicted. Our model appears to be accurate and relatively unbiased, however is conservative at high disease incidence.View full textDownload full textKeywordsrepeated holdout, cross validation, Lyme disease, landscape characterization, model validationRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/09603123.2011.588320