Crowdsourced Photographs as an Effective Method for Large-Scale Passive Tick Surveillance

Human risk to tick encounters in the southeastern United States estimated with spatial distribution modeling

Expanding geographic distribution and increased populations of ticks has resulted in an upsurge of human-tick encounters in the United States (US), leading to an increase in tickborne disease reporting. Limited knowledge of the broadscale spatial range of tick species is heightened by a rapidly changing environment. Therefore, we partnered with the Forest Inventory and Analysis (FIA) program of the Forest Service, U.S. Department of Agriculture and used passive tick surveillance to better understand spatiotemporal variables associated with foresters encountering three tick species (Amblyomma americanum L., Dermacentor variabilis Say, and Ixodes scapularis L.) in the southeastern US. Eight years (2014-2021) of tick encounter data were used to fit environmental niche and generalized linear models to predict where and when ticks are likely to be encountered. Our results indicate temporal and environmental partitioning of the three species. Ixodes scapularis were more likely to be encountered in the autumn and winter seasons and associated with soil organic matter, vegetation indices, evapotranspiration, temperature, and gross primary productivity. By contrast, A. americanum and D. variabilis were more likely to be encountered in spring and summer seasons and associated with elevation, landcover, temperature, dead belowground biomass, vapor pressure, and precipitation. Regions in the southeast least suitable for encountering ticks included the Blue Ridge, Mississippi Alluvial Plain, and the Southern Florida Coastal Plain, whereas suitable regions included the Interior Plateau, Central Appalachians, Ozark Highlands, Boston Mountains, and the Ouachita Mountains. Spatial and temporal patterns of different tick species can inform outdoorsmen and the public on tick avoidance measures, reduce tick populations by managing suitable tick habitats, and monitoring areas with unsuitable tick habitat for potential missed encounters.

Self-reported tick exposure as an indicator of Lyme disease risk in an endemic region of Quebec, Canada

BACKGROUND

Lyme disease (LD) and other tick-borne diseases are emerging across Canada. Spatial and temporal LD risk is typically estimated using acarological surveillance and reported human cases, the former not considering human behavior leading to tick exposure and the latter occurring after infection.

OBJECTIVES

The primary objective was to explore, at the census subdivision level (CSD), the associations of self-reported tick exposure, alternative risk indicators (predicted tick density, eTick submissions, public health risk level), and ecological variables (Ixodes scapularis habitat suitability index and cumulative degree days > 0 °C) with incidence proportion of LD. A secondary objective was to explore which of these predictor variables were associated with self-reported tick exposure at the CSD level.

METHODS

Self-reported tick exposure was measured in a cross-sectional populational health survey conducted in 2018, among 10,790 respondents living in 116 CSDs of the Estrie region, Quebec, Canada. The number of reported LD cases per CSD in 2018 was obtained from the public health department. Generalized linear mixed-effets models accounting for spatial autocorrelation were built to fulfill the objectives.

RESULTS

Self-reported tick exposure ranged from 0.0 % to 61.5 % (median 8.9 %) and reported LD incidence rates ranged from 0 to 324 cases per 100,000 person-years, per CSD. A positive association was found between self-reported tick exposure and LD incidence proportion (ß = 0.08, CI = 0.04,0.11, p < 0.0001). The best-fit model included public health risk level (AIC: 144.2), followed by predicted tick density, ecological variables, self-reported tick exposure and eTick submissions (AIC: 158.4, 158.4, 160.4 and 170.1 respectively). Predicted tick density was the only significant predictor of self-reported tick exposure (ß = 0.83, CI = 0.16,1.50, p = 0.02).

DISCUSSION

This proof-of-concept study explores self-reported tick exposure as a potential indicator of LD risk using populational survey data. This approach may offer a low-cost and simple tool for evaluating LD risk and deserves further evaluation.

EVIDENCE OF PROTOZOAN AND BACTERIAL INFECTION AND CO-INFECTION AND PARTIAL BLOOD FEEDING IN THE INVASIVE TICK HAEMAPHYSALIS LONGICORNIS IN PENNSYLVANIA

The Asian longhorned tick, Haemaphysalis longicornis, an invasive tick species in the United States, has been found actively host-seeking while infected with several human pathogens. Recent work has recovered large numbers of partially engorged, host-seeking H. longicornis, which together with infection findings raises the question of whether such ticks can reattach to a host and transmit pathogens while taking additional bloodmeals. Here we conducted molecular blood meal analysis in tandem with pathogen screening of partially engorged, host-seeking H. longicornis to identify feeding sources and more inclusively characterize acarological risk. Active, statewide surveillance in Pennsylvania from 2020 to 2021 resulted in the recovery of 22/1,425 (1.5%) partially engorged, host-seeking nymphal and 5/163 (3.1%) female H. longicornis. Pathogen testing of engorged nymphs detected 2 specimens positive for Borrelia burgdorferi sensu lato, 2 for Babesia microti, and 1 co-infected with Bo. burgdorferi s.l. and Ba. microti. No female specimens tested positive for pathogens. Conventional PCR blood meal analysis of H. longicornis nymphs detected avian and mammalian hosts in 3 and 18 specimens, respectively. Mammalian blood was detected in all H. longicornis female specimens. Only 2 H. longicornis nymphs produced viable sequencing results and were determined to have fed on black-crowned night heron, Nycticorax nycticorax. These data are the first to molecularly confirm H. longicornis partial blood meals from vertebrate hosts and Ba. microti infection and co-infection with Bo. burgdorferi s.l. in host-seeking specimens in the United States, and the data help characterize important determinants indirectly affecting vectorial capacity. Repeated blood meals within a life stage by pathogen-infected ticks suggest that an understanding of the vector potential of invasive H. longicornis populations may be incomplete without data on their natural host-seeking behaviors and blood-feeding patterns in nature.

Analysis of the complete mitochondrial genomes of Dermacentor albipictus suggests a species complex

Dermacentor albipictus is a one-host tick broadly distributed across North America. There are two easily recognizable color variants - ornate and inornate/brown - that have been taxonomically synonymized. Based on mt-cox1 and mt-16S data, there is also evidence for two genetic lineages which do not match the color variants. We present for the first time the complete mitochondrial genomes of the two color variants of D. albipictus including representatives of each lineage. The AT-rich genomes are 14,822 bp - 14,865 bp in length and contain 13 protein coding genes, 2 ribosomal RNA genes and 22 transfer RNA genes, arranged in the conserved type 3 metastriate mitochondrial genome order. The overall differences were 10.66% between the mitochondrial genomes of D. albipictus ornate variant lineage 1 and lineage 2, 10.51% between lineage 1 and inornate/brown variant and 5.87% between lineage 2 and inornate/brown variant. The inornate/brown variant did not form a separate lineage and all inornate isolates were found to belong to lineage 2. Ornate variant isolates occurred in both lineage 1 and 2. The high divergence of the mitochondrial genome suggests that D. albipictus may represent a species complex. Other barcoding genes that may help capture the genetic differences between color and lineage variants include nad1, nad2, nad5, cox1 and atp8 loci. The mtDNA data generated in this study are available in GenBank (Accession numbers: OM678457 - OM678459 and ON032564 - ON032573) for future studies on tick taxonomy, phylogenetics and molecular epidemiology.

The wild life of ticks: Using passive surveillance to determine the distribution and wildlife host range of ticks and the exotic Haemaphysalis longicornis, 2010-2021

Background

We conducted a large-scale, passive regional survey of ticks associated with wildlife of the eastern United States. Our primary goals were to better assess the current geographical distribution of exotic Haemaphysalis longicornis and to identify potential wild mammalian and avian host species. However, this large-scale survey also provided valuable information regarding the distribution and host associations for many other important tick species that utilize wildlife as hosts.

Methods

Ticks were opportunistically collected by cooperating state and federal wildlife agencies. All ticks were placed in the supplied vials and host information was recorded, including host species, age, sex, examination date, location (at least county and state), and estimated tick burden. All ticks were identified to species using morphology, and suspect H. longicornis were confirmed through molecular techniques.

Results

In total, 1940 hosts were examined from across 369 counties from 23 states in the eastern USA. From these submissions, 20,626 ticks were collected and identified belonging to 11 different species. Our passive surveillance efforts detected exotic H. longicornis from nine host species from eight states. Notably, some of the earliest detections of H. longicornis in the USA were collected from wildlife through this passive surveillance network. In addition, numerous new county reports were generated for Amblyomma americanum, Amblyomma maculatum, Dermacentor albipictus, Dermacentor variabilis, and Ixodes scapularis.

Conclusions

This study provided data on ticks collected from animals from 23 different states in the eastern USA between 2010 and 2021, with the primary goal of better characterizing the distribution and host associations of the exotic tick H. longicornis; however, new distribution data on tick species of veterinary or medical importance were also obtained. Collectively, our passive surveillance has detected numerous new county reports for H. longicornis as well as I. scapularis. Our study utilizing passive wildlife surveillance for ticks across the eastern USA is an effective method for surveying a diversity of wildlife host species, allowing us to better collect data on current tick distributions relevant to human and animal health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05425-1.

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.

Monitoring Trends in Distribution and Seasonality of Medically Important Ticks in North America Using Online Crowdsourced Records from iNaturalist

Simple Summary

An increasing number of cases of tick-borne diseases are being reported across North America and in new areas. This has been linked to the spread of ticks, primarily the blacklegged tick Ixodes scapularis and the lone star tick Amblyomma americanum, into new geographical regions. Tick surveillance systems have played an important role in monitoring the changing distributions of these ticks and have benefitted greatly from including data collected by members of the public through citizen or community science projects. Enlisting the help of community scientists is an economical way to collect large amounts of data over a wide geographical area, and participants can also benefit by receiving information relevant to their tick encounter, for example regarding tick-borne disease symptoms. This study examined tick observations from the online image-based biological recording platform iNaturalist to evaluate its use as an extra tool to collect information on expanding tick distributions. The distribution and seasonality of iNaturalist tick observations were found to accurately represent those of the studied species and identified potential new areas of tick expansion. Free-to-access iNaturalist data is a highly cost-effective method to support existing tick surveillance strategies to aid preparedness and response in emerging areas of tick establishment.

Abstract

Recent increases in the incidence and geographic range of tick-borne diseases in North America are linked to the range expansion of medically important tick species, including Ixodes scapularis, Amblyomma americanum, and Amblyomma maculatum. Passive tick surveillance programs have been highly successful in collecting information on tick distribution, seasonality, host-biting activity, and pathogen infection prevalence. These have demonstrated the power of citizen or community science participation to collect country-wide, epidemiologically relevant data in a resource-efficient manner. This study examined tick observations from the online image-based biological recording platform iNaturalist to evaluate its use as an effective tool for monitoring the distributions of A. americanum, A. maculatum, I. scapularis, and Dermacentor in the United States and Canada. The distribution and seasonality of iNaturalist tick observations were found to accurately represent those of the studied species. County-level iNaturalist tick occurrence data showed good agreement with other data sources in documented areas of I. scapularis and A. americanum establishment, and highlighted numerous previously unreported counties with iNaturalist observations of these species. This study supports the use of iNaturalist data as a highly cost-effective passive tick surveillance method that can complement existing surveillance strategies to update tick distributions and identify new areas of tick establishment.

Identification of Hard Ticks in the United States: A Practical Guide for Clinicians and Pathologists

ABSTRACT

According to guidelines published by the Infectious Disease Society of America, Lyme disease prophylaxis is possible if a tick can be identified as Ixodes scapularis (nymphal or adult) within 72 hours of tick removal. However, a recent survey of medical practitioners indicates generally poor proficiency in tick identification. In this study, we provide a simple, practical guide to aid medical practitioners in identifying the most commonly encountered human biting ticks of North America.

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.

Middle-School Student Engagement in a Tick Testing Community Science Project

Simple Summary

Lyme disease is a common tickborne illness endemic to many countries, including the United States. Scientists have a role to play in disseminating public health knowledge to decrease the prevalence of tickborne disease, which can include encouraging preventive behaviors and recognizing the early signs of the disease. Middle-school students are at significant risk of developing Lyme disease and an ideal population to engage in community-based science, since these experiences provide valuable opportunities for career explorations and to extend the students’ understanding of science. Our work shows that the students can meaningfully contribute to research by generating samples that can be used to test whether the ticks contain pathogens.

Abstract

Studies of tickborne illness have benefited from interactions between scientists and community members. Most participants in community science projects are well-educated adults, but there are anticipated benefits from engaging younger students in research. We evaluated whether an outreach experience for rural middle-school students promoted student interest in science and resulted in the generation of samples that could be used for tick testing to assess disease risk. Middle-school students from 78 Wisconsin communities developed interdisciplinary hypotheses about the spread of Lyme disease, identified ticks, and extracted DNA from ticks to assess the prevalence of pathogens Borrelia burgdorferi, Anaplasma phagocytophillium, and Babesia microti. As a result of this intervention, students were able to successfully complete the research protocol and explain the rationale for completing the experiment. Of student participants, 84.7% reported no difficulty completing the protocol, 66% of the student samples gave reliable PCR results, and 76% of students reported interest in participating in similar experiments. Our study shows that tick outreach programs that incorporate community-based science promote knowledge about Lyme disease, facilitate engagement between students and scientists, and generate samples that can be successfully utilized for pathogen testing.

Identification of public submitted tick images: A neural network approach

Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated with encountered ticks and by providing researchers and public health agencies with additional data on tick activity and geographic range. Here we outline the requirements for such a system, present a model that meets those requirements, and discuss remaining challenges and frontiers in automated tick identification. We compiled a user-generated dataset of more than 12,000 images of the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. We used image augmentation to further increase the size of our dataset to more than 90,000 images. Here we report the development and validation of a convolutional neural network which we call "TickIDNet," that scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network's ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet's performance can be increased by using confidence thresholds to introduce an "unsure" class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.

Crowdsourced Tick Image-Informed Updates to U.S. County Records of Three Medically Important Tick Species

Burgeoning cases of tick-borne disease present a significant public health problem in the United States. Passive tick surveillance gained traction as an effective way to collect epidemiologic data, and in particular, photograph-based tick surveillance can complement in-hand tick specimen identification to amass distribution data and related encounter demographics. We compared the Federal Information Processing Standards (FIPS) code of tick photos submitted to a free public identification service (TickSpotters) from 2014 to 2019 to published nationwide county reports for three tick species of medical concern: Ixodes scapularis Say (Ixodida: Ixodidae), Ixodes pacificus Cooley and Kohls (Ixodida: Ixodidae), and Amblyomma americanum Linneaus (Ixodida: Ixodidae). We tallied the number of TickSpotters submissions for each tick species according to "Reported" or "Established" criteria per county, and found that TickSpotters submissions represented more than half of the reported counties of documented occurrence, and potentially identified hundreds of new counties with the occurrence of these species. We detected the largest number of new county reports of I. scapularis presence in Michigan, North Carolina, and Texas. Tick image submissions revealed potentially nine new counties of occurrence for I. pacificus, and we documented the largest increase in new county reports of A. americanum in Kentucky, Illinois, Indiana, and Ohio. These findings demonstrate the utility of crowdsourced photograph-based tick surveillance as a complement to other tick surveillance strategies in documenting tick distributions on a nationwide scale, its potential for identifying new foci, and its ability to highlight at-risk localities that might benefit from tick-bite prevention education.

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.

Comparing Contributions of Passive and Active Tick Collection Methods to Determine Establishment of Ticks of Public Health Concern Within Illinois

In Illinois, between 1990 and 2017, tick-borne diseases in humans increased 10-fold, yet we have insufficient information on when and where people are exposed to vector ticks (Ixodida: Ixodidae). The aims of our research were to compare contributions of passive and active tick collection methods in determining establishment of ticks of public health concern and obtain information on tick distributions within Illinois. We used three surveillance strategies within the Illinois Tick Inventory Collaboration Network to gather information about the ticks of public health concern: 1) passive collection (voluntary submission by the public); 2) systematic collection (biweekly active surveillance); and 3) special collections (active collections in locations of special interest). Of collected adult and nymphal ticks, 436 were from passive collections, 142 from systematic collections, and 1,270 from special collections. Tick species distribution status changed in 36 counties. Our data provide noteworthy updates to distribution maps for use by public health agencies to develop prevention and control strategies. Additionally, the program built a network of collaborations and partnerships to support future tick surveillance efforts within Illinois and highlighted how the combination of the three surveillance strategies can be used to determine geographic spread of ticks, pinpoint locations in need of more surveillance, and help with long-term efforts that support phenology studies.

Using convolutional neural networks for tick image recognition - a preliminary exploration

Smartphone cameras and digital devices are increasingly used in the capture of tick images by the public as citizen scientists, and rapid advances in deep learning and computer vision has enabled brand new image recognition models to be trained. However, there is currently no web-based or mobile application that supports automated classification of tick images. The purpose of this study was to compare the accuracy of a deep learning model pre-trained with millions of annotated images in Imagenet, against a shallow custom-build convolutional neural network (CNN) model for the classification of common hard ticks present in anthropic areas from northeastern USA. We created a dataset of approximately 2000 images of four tick species (Ixodes scapularis, Dermacentor variabilis, Amblyomma americanum and Haemaphysalis sp.), two sexes (male, female) and two life stages (adult, nymph). We used these tick images to train two separate CNN models - ResNet-50 and a simple shallow custom-built. We evaluated our models' performance on an independent subset of tick images not seen during training. Compared to the ResNet-50 model, the small shallow custom-built model had higher training (99.7%) and validation (99.1%) accuracies. When tested with new tick image data, the shallow custom-built model yielded higher mean prediction accuracy (80%), greater confidence of true detection (88.7%) and lower mean response time (3.64 s). These results demonstrate that, with limited data size for model training, a simple shallow custom-built CNN model has great prospects for use in the classification of common hard ticks present in anthropic areas from northeastern USA.

Potential for online crowdsourced biological recording data to complement surveillance for arthropod vectors

Voluntary contributions by citizen scientists can gather large datasets covering wide geographical areas, and are increasingly utilized by researchers for multiple applications, including arthropod vector surveillance. Online platforms such as iNaturalist accumulate crowdsourced biological observations from around the world and these data could also be useful for monitoring vectors. The aim of this study was to explore the availability of observations of important vector taxa on the iNaturalist platform and examine the utility of these data to complement existing vector surveillance activities. Of ten vector taxa investigated, records were most numerous for mosquitoes (Culicidae; 23,018 records, 222 species) and ticks (Ixodida; 16,214 records, 87 species), with most data from 2019–2020. Case studies were performed to assess whether images associated with records were of sufficient quality to identify species and compare iNaturalist observations of vector species to the known situation at the state, national and regional level based on existing published data. Firstly, tick data collected at the national (United Kingdom) or state (Minnesota, USA) level were sufficient to determine seasonal occurrence and distribution patterns of important tick species, and were able to corroborate and complement known trends in tick distribution. Importantly, tick species with expanding distributions (Haemaphysalis punctata in the UK, and Amblyomma americanum in Minnesota) were also detected. Secondly, using iNaturalist data to monitor expanding tick species in Europe (Hyalomma spp.) and the USA (Haemaphysalis longicornis), and invasive Aedes mosquitoes in Europe, showed potential for tracking these species within their known range as well as identifying possible areas of expansion. Despite known limitations associated with crowdsourced data, this study shows that iNaturalist can be a valuable source of information on vector distribution and seasonality that could be used to supplement existing vector surveillance data, especially at a time when many surveillance programs may have been interrupted by COVID-19 restrictions.

Assessing Public Tick Identification Ability and Tick Bite Riskiness Using Passive Photograph-Based Crowdsourced Tick Surveillance

Tick identification is critical for assessing disease risk from a tick bite and for determining requisite treatment. Data from the University of Rhode Island's TickEncounter Resource Center's photo-based surveillance system, TickSpotters, indicate that users incorrectly identified their submitted specimen 83% of the time. Of the top four most commonly submitted tick species, western blacklegged ticks (Ixodes pacificus Cooley & Kohls [Ixodida: Ixodidae]) had the largest proportion of unidentified or misidentified submissions (87.7% incorrectly identified to species), followed by lone star ticks (Amblyomma americanum Linneaus [Ixodida: Ixodidae]; 86.8% incorrect), American dog ticks (Dermacentor variabilis Say [Ixodida: Ixodidae]; 80.7% incorrect), and blacklegged ticks (Ixodes scapularis Say [Ixodida: Ixodidae]; 77.1% incorrect). More than one quarter of participants (26.3%) submitted photographs of ticks that had been feeding for at least 2.5 d, suggesting heightened risk. Logistic regression generalized linear models suggested that participants were significantly more likely to misidentify nymph-stage ticks than adult ticks (odds ratio [OR] = 0.40, 95% confidence interval [CI]: 0.23, 0.68, P < 0.001). Ticks reported on pets were more likely to be identified correctly than those found on humans (OR = 1.07, 95% CI: 1.01-2.04, P < 0.001), and ticks feeding for 2.5 d or longer were more likely to be misidentified than those having fed for one day or less (OR = 0.43, 95% CI: 0.29-0.65, P < 0.001). State and region of residence and season of submission did not contribute significantly to the optimal model. These findings provide targets for future educational efforts and underscore the value of photograph-based tick surveillance to elucidate these knowledge gaps.

Benefits and Drawbacks of Citizen Science to Complement Traditional Data Gathering Approaches for Medically Important Hard Ticks (Acari: Ixodidae) in the United States

Tick-borne diseases are increasing in North America. Knowledge of which tick species and associated human pathogens are present locally can inform the public and medical community about the acarological risk for tick bites and tick-borne infections. Citizen science (also called community-based monitoring, volunteer monitoring, or participatory science) is emerging as a potential approach to complement traditional tick record data gathering where all aspects of the work is done by researchers or public health professionals. One key question is how citizen science can best be used to generate high-quality data to fill knowledge gaps that are difficult to address using traditional data gathering approaches. Citizen science is particularly useful to generate information on human-tick encounters and may also contribute to geographical tick records to help define species distributions across large areas. Previous citizen science projects have utilized three distinct tick record data gathering methods including submission of: 1) physical tick specimens for identification by professional entomologists, 2) digital images of ticks for identification by professional entomologists, and 3) data where the tick species and life stage were identified by the citizen scientist. We explore the benefits and drawbacks of citizen science, relative to the traditional scientific approach, to generate data on tick records, with special emphasis on data quality for species identification and tick encounter locations. We recognize the value of citizen science to tick research but caution that the generated information must be interpreted cautiously with data quality limitations firmly in mind to avoid misleading conclusions.

Predicting the current and future distribution of the western black-legged tick, Ixodes pacificus, across the Western US using citizen science collections

In the twenty-first century, ticks and tick-borne diseases have expanded their ranges and impact across the US. With this spread, it has become vital to monitor vector and disease distributions, as these shifts have public health implications. Typically, tick-borne disease surveillance (e.g., Lyme disease) is passive and relies on case reports, while disease risk is calculated using active surveillance, where researchers collect ticks from the environment. Case reports provide the basis for estimating the number of cases; however, they provide minimal information on vector population or pathogen dynamics. Active surveillance monitors ticks and sylvatic pathogens at local scales, but it is resource-intensive. As a result, data are often sparse and aggregated across time and space to increase statistical power to model or identify range changes. Engaging public participation in surveillance efforts allows spatially and temporally diverse samples to be collected with minimal effort. These citizen-driven tick collections have the potential to provide a powerful tool for tracking vector and pathogen changes. We used MaxEnt species distribution models to predict the current and future distribution of Ixodes pacificus across the Western US through the use of a nationwide citizen science tick collection program. Here, we present niche models produced through citizen science tick collections over two years. Despite obvious limitations with citizen science collections, the models are consistent with previously-predicted species ranges in California that utilized more than thirty years of traditional surveillance data. Additionally, citizen science allows for an expanded understanding of I. pacificus distribution in Oregon and Washington. With the potential for rapid environmental changes instigated by a burgeoning human population and rapid climate change, the development of tools, concepts, and methodologies that provide rapid, current, and accurate assessment of important ecological qualities will be invaluable for monitoring and predicting disease across time and space.