A Computer Vision Approach to Identifying Ticks Related to Lyme Disease.

Background: Lyme disease (caused by Borrelia burgdorferi) is an infectious disease transmitted to humans by a bite from infected blacklegged ticks (Ixodes scapularis) in eastern North America. Lyme disease can be prevented if antibiotic prophylaxis is given to a patient within 72 hours of a blacklegged tick bite. Therefore, recognizing a blacklegged tick could facilitate the management of Lyme disease. Methods: In this work, we build an automated detection tool that can differentiate blacklegged ticks from other tick species using advanced computer vision approaches in real-time. Specially, we use convolution neural network models, trained end-to-end, to classify tick species. Also, advanced knowledge transfer techniques are adopted to improve the performance of convolution neural network models. Results: Our best convolution neural network model achieves 92% accuracy on unseen tick species. Conclusion: Our proposed vision-based approach simplifies tick identification and contributes to the emerging work on public health surveillance of ticks and tick-borne diseases. In addition, it can be integrated with the geography of exposure and potentially be leveraged to inform the risk of Lyme disease infection. This is the first report of using deep learning technologies to classify ticks, providing the basis for automation of tick surveillance, and advancing tick-borne disease ecology and risk management.

American dog ticks along their expanding range edge in Ontario, Canada.

The American dog tick, Dermacentor variabilis, is a tick of public and veterinary health importance in North America. Using passive tick surveillance data, we document distribution changes for the American dog tick in Ontario, Canada, from 2010 through 2018. Dermacentor variabilis submissions from the public were geocoded and aggregated-from large to small administrative geographies-by health region, public health unit (PHU) and Forward Sortation Area (FSA). PHU hot spots with high rates of D. variabilis submissions were (1) Brant County, Haldimand-Norfolk and Niagara Regional in the Central West region and (2) Lambton and Winsor-Essex County in the South West region. The number of established D. variabilis populations with ≥ 6 submissions per year increased significantly during the study at regional (PHUs: 22 to 31) and local (FSAs: 27 to 91) scales. The range of D. variabilis increased similarly to the positive control (Ixodes scapularis) during the study and in contrast to the static range of the negative control (Ixodes cookei). Submission hot spots were in warmer, low elevation areas with poorly drained soils, compared to the province's low submission areas. Dermacentor variabilis is spreading in Ontario and continued research into their vector ecology is required to assess medicoveterinary health risks.