From July 2006 through August 2017, a passive surveillance study of Ixodes ticks submitted from California, Oregon, and Washington was conducted by the TickReport program at the University of Massachusetts, Amherst. In total, 549 human-biting Ixodes ticks were submitted comprising both endemic and nonendemic species. We found that 430 endemic ticks were from 3 Ixodes species: Ixodes pacificus, Ixodes spinipalpis, and Ixodes angustus, whereas Ixodes scapularis (n = 111) was the most common species among the 119 nonendemic ticks. The submission peak for nymphal I. pacificus and I. spinipalpis was June, while submission peak for adult I. pacificus and nymphal I. angustus was April and September, respectively. Endemic ticks commonly attached to the lower extremities of their victims, and individuals younger than 9 years old were frequently bitten. The infection prevalence of Borrelia burgdorferi sensu lato, Borrelia miyamotoi, and Anaplasma phagocytophilum in I. pacificus ticks was 1.31%, 1.05%, and 0.52%, respectively, and the prevalence of B. burgdorferi s. l. and A. phagocytophilum in I. spinipalpis ticks was 14.29% and 10.71%, respectively. Furthermore, two species within the B. burgdorferi s. l. complex were detected in West Coast ticks: B. burgdorferi sensu stricto and Borrelia lanei. I. spinipalpis had the highest Borrelia prevalence among endemic ticks, and it was caused exclusively by B. lanei. Borrelia mayonii, Babesia microti, and Ehrlichia muris-like agent were not detected in these endemic ticks. In this study, we show that many nonendemic Ixodes ticks (119/549) are most likely acquired from travel to a different geographic region. We report cases of conventionally recognized nonhuman feeders (I. spinipalpis and I. angustus) parasitizing humans. The highest pathogen prevalence in I. spinipalpis may indicate a larger public health threat than previously thought, and the enzootic life cycle and pathogenicity of B. lanei warrant further study.

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Ehrlichia muris is an agent of human ehrlichiosis. To determine its geographic spread in the United States, during 2016–2017, we tested 8,760 ticks from 45 states. A distinct clade of E. muris found in 3 Ixodes cookei ticks from the northeastern United States suggests transmission by these ticks in this region.

A wide range of pathogens, such as bacteria, viruses, and parasites can be transmitted by ticks and can cause diseases, such as Lyme disease, anaplasmosis, or Rocky Mountain spotted fever. Landscape and climate changes are driving the geographic range expansion of important tick species. The morphological identification of ticks is critical for the assessment of disease risk; however, this process is time-consuming, costly, and requires qualified taxonomic specialists. To address this issue, we constructed a tick identification tool that can differentiate the most encountered human-biting ticks, Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis, by implementing artificial intelligence methods with deep learning algorithms. Many convolutional neural network (CNN) models (such as VGG, ResNet, or Inception) have been used for image recognition purposes but it is still a very limited application in the use of tick identification. Here, we describe the modified CNN-based models which were trained using a large-scale molecularly verified dataset to identify tick species. The best CNN model achieved a 99.5% accuracy on the test set. These results demonstrate that a computer vision system is a potential alternative tool to help in prescreening ticks for identification, an earlier diagnosis of disease risk, and, as such, could be a valuable resource for health professionals.

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