Predicting spatio‐temporal population patterns of Borrelia burgdorferi, the Lyme disease pathogen. Issue 11 (13th September 2022)

• Record Type: Journal Article
• Authors: Tran, Tam; Prusinski, Melissa A.; White, Jennifer L.; Falco, Richard C.; Kokas, John; Vinci, Vanessa; Gall, Wayne K.; Tober, Keith J.; Haight, Jamie; Oliver, JoAnne; Sporn, Lee Ann; Meehan, Lisa; Banker, Elyse; Backenson, P. Bryon; Jensen, Shane T.; Brisson, Dustin
• Is Part Of: Journal of applied ecology. Volume 59:Issue 11(2022)
• Page Start: 2779
• Page End: 2789
• Publication Date: 2022-09-13
• Subjects: Borrelia burgdorferi -- environmental change -- infection prevalence -- Lyme disease -- population dynamics -- predictive models -- tick‐borne bacterium -- vector‐borne pathogen; Agriculture -- Periodicals; Biology, Economic -- Periodicals; Agricultural ecology -- Periodicals; Applied ecology -- Periodicals; 577
• DOI: 10.1111/1365-2664.14274
• Languages: English
• ISSNs: 0021-8901

Abstract

Abstract: 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. Abstract : 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.