Phase II Amount
$1,005,392
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 overhalf-a-million Americans each year. Due to lack of effective vaccines for tick-borne diseases, preventionof tick bites and early tick bite treatment is the primary focus of disease mitigation. Tick vectorsurveillance-monitoring an area to understand tick species composition, abundance, and spatialdistribution-is key to providing the public with accurate and up-to-date information when they are inareas of high risk, and enabling precision vector control when necessary. Despite the importance of vectorsurveillance, current practices are highly resource intensive and require significant labor and time tocollect and identify vector specimens. Acarologist or field taxonomist expertise is a limited resourcerequired for tick identification, creating a significant capability barrier for national tick surveillancepractice. While mobile applications to facilitate passive surveillance and reporting of human-tickencounters have grown in popularity, variable image quality, limited engagement, and scientistmisidentification of rare, invasive, or morphologically similar tick species hinder the scalability of thisapproach. To date, no automated solutions exist to build tick identification capacity. We seek to advancePhase I work that successfully achieved an imaging and automated identification system capable ofinstantaneously and accurately identifying twelve adult tick species with 98% accuracy. This proposalwill first improve the Phase I optical design for scalability to accommodate imaging of additionalintra-specific tick species variability as nymphs, adult males, and unfed or engorged adult females. Inparallel, we develop methods to optimize quality of guided user imaging of ticks in a mobile appapproach for the general public. This will enable the development of a representative image database withpartners including TickSpotters, TickCheck, the Walter Reed Biosystems Unit (WRBU), and others. Theresulting database will be used to train, validate, test and deploy high-accuracy computer vision models intwo tick identification products for professional public health and the general public. Ultimately theapproaches developed here will enable vector management organizations to leverage image recognition ina practical system that will increase capacity and capability for biosurveillance, and equip the generalpublic with improved tools to identify ticks during a human-tick encounter.
Public Health Relevance Statement: 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.
Project Terms: <21+ years old>