An analysis of companion animal tick encounters as revealed by photograph-based crowdsourced data

All for One Health and One Health for All: Considerations for Successful Citizen Science Projects Conducting Vector Surveillance from Animal Hosts

Many vector-borne diseases that affect humans are zoonotic, often involving some animal host amplifying the pathogen and infecting an arthropod vector, followed by pathogen spillover into the human population via the bite of the infected vector. As urbanization, globalization, travel, and trade continue to increase, so does the risk posed by vector-borne diseases and spillover events. With the introduction of new vectors and potential pathogens as well as range expansions of native vectors, it is vital to conduct vector and vector-borne disease surveillance. Traditional surveillance methods can be time-consuming and labor-intensive, especially when surveillance involves sampling from animals. In order to monitor for potential vector-borne disease threats, researchers have turned to the public to help with data collection. To address vector-borne disease and animal conservation needs, we conducted a literature review of studies from the United States and Canada utilizing citizen science efforts to collect arthropods of public health and veterinary interest from animals. We identified common stakeholder groups, the types of surveillance that are common with each group, and the literature gaps on understudied vectors and populations. From this review, we synthesized considerations for future research projects involving citizen scientist collection of arthropods that affect humans and animals.

A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models

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.

Community engaged tick surveillance and tickMAP as a public health tool to track the emergence of ticks and tick-borne diseases in New York

A community engaged passive surveillance program was utilized to acquire ticks and associated information throughout New York state. Ticks were speciated and screened for several tick-borne pathogens. Of these ticks, only I. scapularis was commonly infected with pathogens of human relevance, including B. burgdorferi, B. miyamotoi, A. phagocytophilum, B. microti, and Powassan virus. In addition, the geographic and temporal distribution of tick species and pathogens was determined. This enabled the construction of a powerful visual analytical mapping tool, tickMAP to track the emergence of ticks and tick-borne pathogens in real-time. The public can use this tool to identify hot-spots of disease emergence, clinicians for supportive evidence during differential diagnosis, and researchers to better understand factors influencing the emergence of ticks and tick-borne diseases in New York. Overall, we have created a community-engaged tick surveillance program and an interactive visual analytical tickMAP that other regions could emulate to provide real-time tracking and an early warning for the emergence of tick-borne diseases.

Tick-Borne Disease Prevention Behaviors Among Participants in a Tick Surveillance System Compared with a Sample Of Master Gardeners

Theory-based approaches to health communication and behavior are increasingly applied to interventions that address poor public tick-borne disease prevention knowledge and practices. We sought to understand the tick-borne disease prevention behaviors among participants in a crowdsourced passive tick surveillance system that employs theory-based messages about tick bite risk and prevention strategies. We administered an electronic survey to a randomly selected sample of passive surveillance system users and compared their responses to those from a nationwide sample of Master Gardeners (MG), a group with heighten tick exposure due to outdoor activity. Over 80% of TickSpotters respondents, and over 75% of MG respondents encountered a tick in the past year. Among both groups, tick checks were the most frequently practiced prevention behavior, with over 70% of people performing them most or all the time after outdoor activity. A greater proportion of MGs used skin repellents such as DEET or picaridin than TickSpotters users, but more than 70% of respondents from both groups reported that they never or only sometimes use permethrin-treatment on clothing, and nearly half of both groups reportedly used no peridomestic tick treatments. TickSpotters respondents overwhelmingly reported recording tick encounter information and saving specimens for identification and testing, while only a small percentage of MGs monitored their tick encounters. These findings suggest that while both TickSpotters and MG groups appear to be practicing some important tick bite prevention behaviors, there remain areas that could benefit from targeted theory-based interventional approaches.

An analysis of companion animal tick encounters as revealed by photograph-based crowdsourced data

Background

Community science is increasingly utilized to track important vectors of companion animal disease, providing a scalable, cost‐effective strategy for identifying new foci, changing phenology, and disease prevalence across wide geographies.

Objectives

We examined photographs of ticks found attached to predominately dogs and cats reported to a photograph‐based tick surveillance program to identify potential areas for improvements in tick prevention education and risk intervention.

Methods

We compared estimated days of tick attachment using a Kruskal–Wallis one‐way analysis of variance, and a Pearson's chi‐square analysis of variance on the number of submissions by host type submitted for each season.

Results

The blacklegged tick (Ixodes scapularis) was the most common species reported (39.8%). Tick photographs submitted were almost entirely adults (89.5%), and ticks found on companion animals exhibited an estimated median engorgement time of 2.5 days. Ixodes scapularis displayed the highest median engorgement of the top tick species found feeding on companion animals (χ² = 98.96, p < 0.001). Ticks were spotted year‐round; during spring and summer, ticks collected from pets represented 15.4 and 12.8% of all submissions, but increased to 28.5 and 35.2% during autumn and winter, respectively.

Conclusions

Crowdsourced data reveal that mostly adult ticks are detected on pets, and they are found at a point in the blood‐feeding process that puts pets at heightened risk for disease transmission. The increase in proportion of ticks found on pets during colder months may reveal a critical knowledge gap amongst pet owners regarding seasonal activity of I. scapularis, a vector of Lyme disease, providing an opportunity for prevention‐education.