Lyme Disease is the most common vector-borne illness in the U.S., and presents a serious public health risks throughout the northern U.S. and Canada. This illness, caused by the bacterial pathogen Borrelia burgdorferi and transmitted primarily by black-legged ticks (Ixodes scapularis), presents unique challenges for epidemic models, as well as unique opportunities for public health interventions. The pathogen is maintained in the environment thanks to a complex interaction of vector and reservoir animal species, over multiple tick life-stages and seasons. Interventions must therefore seek to interrupt the transmission to incidental hosts (humans and domestic dogs) – a strategy which has primarily focused on prevention of exposure to the tick vector. In this work, we present a Bayesian analysis of data from a reservoir vaccination study designed to interrupt transmission of B. burgdorferi from one generation of ticks to the next by promoting the immunocompetency of the primary reservoir species, white footed mice (P. leucopus). We introduce a framework for generalizing these results to more widespread, targeted intervention planning by modeling tick prevalence in the environment and exposure to human populations.