Vector-borne diseases (VBDs) are the most common types of emerging infectious diseases in the world and constitute major threats to public health. Understanding the interplay between the environment, vector, and pathogen that expedite disease spread remains a challenge. The overarching goal of this thesis is to identify the key environmental drivers that have led to the emergence of VBDs by focusing on the Lyme disease system. Statistical models were built on extensive collections of Ixodes scapularis nymphal ticks and the obligate parasite responsible for Lyme disease, Borrelia burgdorferi, across New York State. My thesis demonstrates that spatio-temporal variation in environmental factors, specifically landscape and climatic features, accounts for the distribution and population sizes of both the tick vector and bacterium pathogen across space and time. The models accurately predict both species population dynamics into future years and new, previously unsampled regions. Consequently, these models were utilized to create fine-scale predictive maps that be used to assess future risk of Lyme disease. These findings have important public health applications, however, the necessary fieldwork for model development is costly in time and labor. Anticipating the real-world challenges these barriers may pose, this thesis also demonstrates a strong correlation between citizen science and active surveillance tick collections. Importantly, this led to models that combined citizen science data and population-level factors to accurately estimate tick population sizes at a statewide scale and across years. Utilizing citizen science data in this way can both facilitate updated ecological distribution maps and open new possibilities for surveillance of VBDs.