Validating negative binomial lyme disease regression model with bootstrap resampling

Various negative binomial regression models have been developed to study Lyme disease in connection to climate and/or landscape factors. However, no internal validation of any of those models has been reported in the literature. This study used bootstrap resampling to conduct an internal validation of a negative binomial regression model on Lyme disease incidence. The model used county-level Lyme disease incidence in thirteen states in the Northeastern United States during 2002-2006 and linked it with several previously identified key landscape and climatic variables used in an earlier study. Results showed that there were significant differences between the outcomes from the initial model and those from bootstrap resampling. Arguably bootstrap resampling, as illustrated in this study, can serve as a sound and valuable means to provide a second line of evidence on model outcomes and shed more insight on variables (e.g., climate and landscape factors) included in the models.