title: Repeated holdout cross-validation of model to estimate risk of Lyme disease by landscape characteristics.
abstract: 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.
authors.affiliation: Environmental Public Health Division, US Environmental Protection Agency (USEPA), NC, USA. hilborn.e@epa.gov
authors.forename: Elizabeth D
authors.initials: ED
authors.lastname: Hilborn
authors.forename: Donald G
authors.initials: DG
authors.lastname: Catanzaro
authors.forename: Laura E
authors.initials: LE
authors.lastname: Jackson
mesh_terms: D015982:Bias; D004777:Environment; D006801:Humans; D015994:Incidence; D008193:Lyme Disease / Q000453:epidemiology* / Q000635:transmission; D008396:Maryland / Q000453:epidemiology; D008954:Models, Biological; D012111:Residence Characteristics; D018570:Risk Assessment / Q000379:methods*