TICK DATA
What follows is a summary of data that this project has generated so far. Most of this research has been conducted by students and teachers at New Philadelphia High School, New Philadelphia, Ohio and is supported by grants from the Ohio Environmental Education Fund and the Entomological Society of America's Chrysalis Fund. Beginning in the 2024-2025 school year, we have been recruiting teachers and students from other school districts to collaborate in this project.
The following map summarizes Borrelia burgdorferi (the etiologic agent of Lyme Disease) prevalence data for Blacklegged Tick (Ixodes scapularis) populations in Tuscarawas County, as well as that of a few populations in Stark and Summit Counties, Ohio. Each pin represents a location that we have surveyed. By clicking on a pin, you can access updated information on the prevalence of Borrelia in the ticks we have sampled there (as "percent positive for Borrelia") and the number of ticks that have been tested from that location.
As of 2023, we found that B. burgdorferi prevalence is quite high almost everywhere we look. We've looked most extensively in Tuscarawas County, where prevalence rates have ranged from 0% to 85% in questing adult ticks. It is worth noting that, of the 28 points sampled in Tuscarawas County in this map, the average B. burgdorferi prevalence was roughly 44 +18 SD %. Although some sites had really small samples of ticks that we tested (e.g., one site had only 2 ticks tested, two others had only 4 ticks tested), what this early sampling effort revealed was that B. burgdorferi and Blacklegged Ticks are quite common across the landscape of southeastern Ohio. Some locations had very high B. burgdorferi prevalence rates, and although we didn't measure tick densities at many sites during these early surveys, we did note differences in tick densities. What factors account for these differences?
How does Borrelia burgdorferi infection rate vary in Blacklegged Tick Populations? (2020-2023)
Is there a relationship between tick densities, B. burgdorferi prevalence, and reported human Lyme Disease case rates?
During the 2023-2024 school year, students at New Philadelphia high school began looking beyond Tuscarawas County to explore relationships between the county-level reported human Lyme Disease (LD) cases per 100,000 and measured tick densities and B. burgdorferi prevalence rates within adult Blacklegged Tick populations. The map at right shows the Ohio Department of Health's reported LD case rates by county as of 2025. As you can see, the LD case rates vary, and counties with the highest numbers of cases are clustered in the east-central part of the state, which coincides with the northern edge of the Western Allegheny Plateau ecoregion.
We compared tick density and B. burgdorferi prevalence in two forests within Tuscarawas County (41 reported cases per 100,000), Stark County (9 reported cases per 100,000), and Summit County (4 reported cases per 100,000) to examine if the number of reported LD cases correlated with tick density and/or B. burgdorferi prevalence. ​​
The following graphic summarizes the results of this study. The map to the left (A) shows the spatial arrangement of the survey locations used in this study. The bars in the graph (B) show the mean values for tick density (gray bars, ticks per 100 m^2), B. burgdorferi prevalence rate (orange bars, %), and reported cases (blue bars, per 100,000). The asterisks in the gray and orange bars show the actual values for each survey location.
On average, there was no significant difference in either tick density or B. burgdorferi prevalence across counties (one-way ANOVA with Brown-Forsythe correction: F2,1.099 = 0.428, P = 0.729 and F2,1.51 = 0.133, P = 0.887, respectively). Obviously, we are dealing with small sample sizes here, but one thing that was clear is that forested habitats across counties can have similar tick densities and B. burgdorferi prevalence. However, these variables, on their own, do not account for the differences in reported cases across counties. ​
Many potential variables can influence reported LD cases. There could be county-level differences in the number of physicians who actually report cases, differences in the number of people seeking medical care, differences in the number of cases correctly diagnosed, differences in the probability of people in a given county coming into contact with ticks, etc. One interesting consideration is the proportion of forest cover, since the Blacklegged Tick is primarily a forest-dependent species. Fifty-three percent of Tuscarawas County is covered in forest, whereas Stark County (25%) and Summit County (30%) have less forest cover, so it seems plausible that a greater amount of forest coverage could underly the higher case rates observed in Tuscarawas County. However, when one compares forest coverage and LD case rates across other heavily forested southeastern Ohio counties, the relationship no longer seems so simple. For instance, while Tuscarawas County has 41 cases per 100,000 and is 53% forested, Athens County has less than 6 cases per 100,000 and is 74% forested.
Is there a relationship between tick densities, B. burgdorferi prevalence, and reported human Lyme Disease case rates across the Appalachian region of Ohio?
Southeastern Ohio is the most heavily forested portion of the state, and so, for the 2024-2025 school year, we began examining how tick density, B. burgdorferi prevalence, and LD case rates varied across this region. To accomplish this task, we collaborated with teachers from 7 counties that formed a transect across this region. Students from each school selected at least one forested site to survey within their county and measured tick density and B. burgdorferi prevalence from questing adult female Blacklegged Ticks. The following map shows 8 counties that were studied during the 2024-2025 school year. They include (from north to south) Stark, Tuscarawas, Harrison, Belmont, Noble, Morgan, Athens, and Ross counties. The teal colored line on the map indicates the boundary of the Western Allegheny Plateau ecoregion, and the green color indicates forest cover. To the right of the graph are shown the county-level LD case rate, % forest cover, and our preliminary B. burgdorferi prevalence results from the 2024-2025 school year. Note that data from two counties were not yet compiled at this writing.
It is important to note that this is just the first year of this effort, so the data are very preliminary (e.g., only one forested site studied in most counties, it was the first year for most of the teachers and students involved, etc.). Still, we did attempt some analyses of observed patterns that will shape future research efforts.
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Regarding the relationship between county-level reported LD cases and B. burgdorferi prevalence rates, we constructed the following regression using our preliminary data.
FIP is "Female Infection Prevalence" for B. burgdorferi. We found no significant correlation between reported LD cases and FIP (Pearson's r, P = 0.681).
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Four sampling locations provided reliable estimates of both tick density and B. burgdorferi infection prevalence at forest edge and interior habitats. These sampling locations differed in forest size, so we examined the relationship between tick density (as density of questing adult female ticks, DOF), B. burgdorferi prevalence (as female infection prevalence, FIP), and forest habitat area. The following graphs show how DOF and FIP varied with forest area.
The first thing to note is that both tick density (A) and B. burgdorferi infection prevalence (B) tended to be higher at forest edges than at forest interiors. Although we did not discover any statistically significant differences, our sample sizes were small, and so we will focus future research efforts on evaluating the effect of edge and interior habitat conditions on these variables.
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The second thing to note is how DOF (A) and FIP (B) for forest edge and interior locations both tended to decrease as forest area increased, and, along with that, how DOF and FIP of edge and interior locations seem to converge as forest area increases. Both DOF and FIP are important measures related to LD risk, and so these data suggest that larger patches of forest habitat may pose a lower LD risk than smaller, more highly fragmented patches. Further effort will be devoted to exploring the role that forest patch size might play in LD risk.