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 prevalence of B. burgdorferi-infected ticks that coincided with increases in human Lyme disease incidence across New York State. 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 in the nymphal infection prevalence of B. burgdorferi across space and time. Importantly, the model identified environmental features reflecting landscape ecology, vertebrate hosts, climatic metrics, climate anomalies and surveillance efforts 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 prevalence of a pathogen at fine geographic scales offers a powerful strategy to mitigate a serious public health threat. Synthesis and applications. A decade of environmental and tick data was collected to create a model that accurately predicts the infection prevalence of Borrelia burgdorferi over space and time. This predictive model can be extrapolated to create a high-resolution risk map of the Lyme disease pathogen for future years that offers an inexpensive approach to improve both ecological management and public health strategies to mitigate disease risk.

Automated summary

Borrelia burgdorferi, the bacterium causing Lyme disease, has become the most common vector-borne disease in the United States over the past few decades. The study found that environmental features significantly affect the population dynamics of B. burgdorferi, and a model using field collections and environmental data accurately predicted the infection prevalence of the bacterium in tick vectors.