Predicting spatio-temporal population patterns of Borrelia burgdorferi, the Lyme disease pathogen
Creators: name: Tran, Tam
name: Prusinski, Melissa A.
name: White, Jennifer L.
name: Falco, Richard C.
name: Kokas, John
name: Vinci, Vanessa
name: Gall, Wayne K.
name: Tober, Keith J.
name: Haight, Jamie
name: Oliver, JoAnne
name: Sporn, Lee Ann
name: Meehan, Lisa
name: Banker, Elyse
name: Backenson, P. Bryon
name: Jensen, Shane T.
name: Brisson, Dustin
Publisher: name: Zenodo
Year: 2022
Subjects: Borrelia burgdorferi
Lyme disease
pathogen
Ixodes scapularis
tick-borne pathogen
tick-borne disease
Bacterium

description: The causative bacterium of Lyme disease, <em>Borrelia burgdorferi</em>, 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 <em>B. burgdorferi</em> 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 <em>B. burgdorferi</em>. Analytical models developed using field collections of nearly 19,000 nymphal <em>Ixodes scapularis </em>and spatially- and temporally-explicit environmental features accurately explained the variation of <em>B. burgdorferi </em>population sizes across space and time. Importantly, the model identified environmental features that can be used to predict the biogeographical patterns of <em>B. burgdorferi-</em>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.
description: 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 Health<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000002<br>Award Number: AI097137Funding provided by: Centers for Disease Control and Prevention<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000030<br>Award Number: U01CK000509Funding provided by: Burroughs Wellcome Fund<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000861<br>Award Number: 1012376Funding provided by: National Institutes of Health<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000002<br>Award Number: AI142572Funding provided by: National Institutes of Health<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000002<br>Award Number: AI137433Funding provided by: National Institutes of Health<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000002<br>Award Number: T32-AI55400-15Funding provided by: National Institutes of Health<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000002<br>Award Number: F31AI133871
description: Model 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.