48: Modeling Seasonal and Coinfection Effects on Lyme Disease Using a Bayesian Hidden Markov Framework

Thomas M. Menino Convention & Exhibition Center 

Lyme disease (LD), transmitted by Ixodes ticks infected with Borrelia burgdorferi, is the most commonly reported vector-borne disease in the United States and continues to expand geographically. Transmission-blocking, reservoir-targeted vaccines (TBRTVs) have been proposed to reduce nymphal infection prevalence and disrupt transmission. Evaluating such interventions requires understanding infection and recovery dynamics over time. Yet, longitudinal infection data are often sparse, irregularly sampled and seasonal. Multi-year longitudinal data on canine Borrellia infection were collected from a cohort of hunting dogs, providing a view into the potential impact of coinfection. In particular, we investigate the relationship between Leishmania infection and Lyme Disease. We propose a Bayesian hierarchical hidden Markov model to characterize infection and recovery processes, allowing transition probabilities to vary across seasons and sites, and to respond to coinfection. Overall, this framework provides a flexible approach for disentangling seasonal and coinfection effects in sparse longitudinal disease data, with important implications for interventions targeting disease burden.

Vaccine

Bayesian hierarchical model

Lyme disease

Leishmania Infection

Borrelia burgdorferi 

Section on Statistics in Epidemiology