Comparative ecological analysis and predictive modeling of tick-borne pathogens

• Published in: Journal of medical entomology 2025-01, Vol.62 (1), p.199-206
• Main Authors: Manley, William, Tran, Tam, Prusinski, Melissa, Brisson, Dustin
• Format: Article
• Language: English
• Subjects: Anaplasma phagocytophilum - isolation & purification; Animals; Babesia microti - isolation & purification; Borrelia burgdorferi - isolation & purification; Ixodes - growth & development; Ixodes - microbiology; Lyme Disease - epidemiology; Lyme Disease - transmission; Models, Biological; Tick-Borne Diseases - epidemiology; Tick-Borne Diseases - microbiology
• ISSN: 0022-2585; 1938-2928; 1938-2928
• DOI: 10.1093/jme/tjae127

Tick-borne diseases constitute the predominant vector-borne health threat in North America. Recent observations have noted a significant expansion in the range of the black-legged tick (Ixodes scapularis Say, Acari: Ixodidae), alongside a rise in the incidence of diseases caused by its transmitted pathogens: Borrelia burgdorferi Johnson (Spirochaetales: Spirochaetaceae), Babesia microti Starcovici (Piroplasmida: Babesiidae), and Anaplasma phagocytophilium Zhu (Rickettsiales: Anaplasmataceae), the causative agents of Lyme disease, babesiosis, and anaplasmosis, respectively. Prior research identified environmental features that influence the ecological dynamics of I. scapularis and B. burgdorferi that can be used to predict the distribution and abundance of these organisms, and thus Lyme disease risk. In contrast, there is a paucity of research into the environmental determinants of B. microti and A. phagocytophilium. Here, we use over a decade of surveillance data to model the impact of environmental features on the infection prevalence of these increasingly common human pathogens in ticks across New York State (NYS). Our findings reveal a consistent northward and westward expansion of B. microti in NYS from 2009 to 2019, while the range of A. phagocytophilum varied at fine spatial scales. We constructed biogeographic models using data from over 650 site-year visits and encompassing more than 250 environmental variables to accurately forecast infection prevalence for each pathogen to a future year that was not included in model training. Several environmental features were identified to have divergent effects on the pathogens, revealing potential ecological differences governing their distribution and abundance. These validated biogeographic models have applicability for disease prevention efforts.