Predicting spatio-temporal population patterns of Borrelia burgdorferi, the Lyme disease pathogen

The causative bacterium of Lyme disease, Borrelia burgdorferi, expanded from an undetected human pathogen into the etiologic agent of the most common vector-borne disease in the United States over the last several decades. Systematic field collections of the tick vector reveal increases in the geographic range and population size of B. burgdorferi that coincided with increases in human Lyme disease incidence across New York State. Here we investigate the impact of environmental features on the population dynamics of B. burgdorferi. Analytical models developed using field collections of nearly 19,000 nymphal Ixodes scapularis and spatially- and temporally-explicit environmental features accurately explained the variation of B. burgdorferi population sizes across space and time. Importantly, the model identified environmental features that can be used to predict the biogeographical patterns of B. burgdorferi-infected ticks into future years and in previously unsampled areas. Forecasting the distribution and abundance of a pathogen at fine geographic scales offers a powerful strategy to mitigate a serious public health threat.R codes include two separate files for compiling data and model development. Data include tick/pathogen collection data for different years, with data grouped by years for training and test data for the model. Relevant R code and tick collection data that were previously published can be found at MendeleyData (doi: https://doi.org/10.17632/rtd52gnbyy.1) with further detail in Supplementary Materials in Tran et al. 2020. Funding provided by: National Institutes of HealthCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000002Award Number: AI097137Funding provided by: Centers for Disease Control and PreventionCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000030Award Number: U01CK000509Funding provided by: Burroughs Wellcome FundCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000861Award Number: 1012376Funding provided by: National Institutes of HealthCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000002Award Number: AI142572Funding provided by: National Institutes of HealthCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000002Award Number: AI137433Funding provided by: National Institutes of HealthCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000002Award Number: T32-AI55400-15Funding provided by: National Institutes of HealthCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000002Award Number: F31AI133871Model selection was performed with R 'mass' package. The stepAIC function performs stepwise model selection by AIC, searching the range of models with the lower component defined as the null model and upper component defined as the full model. Model search arguments are set as "both" such that search strategy is bi-directional, with steps =1000, and k = 2