Abstract. The incidence of US tick-borne diseases has more than doubled in the last two decades. Today, Lyme disease is the most common vector-borne disease in the United States, impacting over half-a-million Americans each year. Due to lack of effective vaccines for tick-borne diseases, prevention of tick bites and early tick bite treatment is the primary focus of disease mitigation. Tick vector surveillance—monitoring an area to understand tick species composition, abundance, and spatial distribution—is key to providing the public with accurate and up-to-date information when they are in areas of high risk, and enabling precision vector control when necessary. Despite the importance of vector surveillance, current practices are highly resource intensive and require significant labor and time to collect and identify vector specimens. Acarologist or field taxonomist expertise is a limited resource required for tick identification, creating a significant capability barrier for national tick surveillance practice. While mobile applications to facilitate passive surveillance and reporting of human-tick encounters have grown in popularity, variable image quality, limited engagement, and scientist misidentification of rare, invasive, or morphologically similar tick species hinder the scalability of this approach. To date, no automated solutions exist to build tick identification capacity. We seek to advance Phase I work that successfully achieved an imaging and automated identification system capable of instantaneously and accurately identifying twelve adult tick species with 98% accuracy. This proposal will first improve the Phase I optical design for scalability to accommodate imaging of additional intra-specific tick species variability as nymphs, adult males, and unfed or engorged adult females. In parallel, we develop methods to optimize quality of guided user imaging of ticks in a mobile app approach for the general public. This will enable the development of a representative image database with partners including TickSpotters, TickCheck, the Walter Reed Biosystems Unit (WRBU), and others. The resulting database will be used to train, validate, test and deploy high-accuracy computer vision models in two tick identification products for professional public health and the general public. Ultimately the approaches developed here will enable vector management organizations to leverage image recognition in a practical system that will increase capacity and capability for biosurveillance, and equip the general public with improved tools to identify ticks during a human-tick encounter.

Project Narrative. Current tick identification methods are highly resource and labor intensive, requiring physical collection of specimens and subsequent identification of species, sex, life stage, and engorgement by an acarologist based on visual morphological inspection. Here we propose to advance a prototype deep learning system capable of instantaneously and accurately identifying twelve medically-relevant adult tick species with 98% accuracy for practical deployment in professional public health and the general public. The resulting products developed through this proposal will ultimately expand tick surveillance capability and capacity, and strengthen public health response to tick-borne diseases.