Landscape and climate factors can predict the prevalence of Lyme disease bacteria

The study, published in Journal of Applied Ecology, unraveling the relationship between two of these players in the ecology of Lyme disease: bacteria and the environment. Led by Tam Tran, who received her PhD from Penn’s Department of Biology at the School of Arts & Sciences, and with mentors Dustin Brisson, a department professor, Shane Jensen of the Wharton School, along with colleagues from the New York State Department of Health research examines how variables such as landscape disturbance and climate affect distribution and abundance B. burgdorferi. The result is a powerful analytical model that can accurately predict the prevalence and distribution of Lyme disease bacteria in the landscape, potentially a useful public health tool to curb disease transmission.

“We know that Lyme disease is a growing public health threat, but we haven’t found great ways to address it. The number of cases continues to grow,” says Tran, who is now a medical student at Virginia Commonwealth University. “What’s exciting about this is that by knowing how the environment affects both the tick system and the bacteria, we can predict where and when there will be higher levels of the pathogen in the landscape.”

In the current study, Tran, Brisson, Jensen and colleagues mainly focused on what factors influenced them B. burgdorferi, the prevalence of which they measured by determining what fraction of the black-legged ticks they sampled were infected with the bacteria. Previous attempts to establish links between Lyme disease and environmental variables have yielded mixed, unclear, or sometimes even conflicting results, Tran says, in part because the “environment” at large can be so diverse.

To create their models, the research team took data from nearly 19,000 black-legged ticks collected from hundreds of sites in upstate New York between 2009 and 2018. They assessed how the numbers of infected and uninfected ticks in hundreds of locations over more than a decade matched local environmental characteristics, which fall into four broad categories:

1) landscape factors such as elevation, fire history, and distance to infrastructure such as roads;

2) host population sizes of vertebrates including humans, bears, birds and deer;

3) monitoring conditions including local temperature and humidity at the time of collection and the effort involved in collecting samples; and

4) Climate measures such as monthly average temperatures, precipitation and days with temperatures below freezing.

By running different groupings of these variables through powerful computer models, the researchers were able to identify which had the greatest impact in determining infectivity rates.

“The key finding was that climate was an overwhelming feature in the model,” says Tran. “Habitat disturbance was also important, and in some cases we found the opposite of what previous studies had suggested.”

While previous analyzes had found that increases in disturbances — things like fires, roads cutting through forests, and fragmented habitats — led to increases in disturbances B. burgdorferi Numbers, the team led by Penn found that less disturbed, more intact habitats were often associated with greater numbers of ticks infected with the bacteria.

After developing a model using the data collected in 2009-18, they then tested how well the model could predict the prevalence and distribution found in the data collected from 2019 onwards.

“We found it to be very accurate,” says Tran. “And what’s great is that a lot of the data we used to create the model is free, which means other places may be able to replicate these results to predict Lyme disease risk, particularly in areas where the climate and landscape are similar to New York.”

Interventions could include public health messages warning visitors to the park, for example of the risk of disease, “to remind them to do their tick checks,” Tran says. Findings could also help guide future land management and harness the power of ecology to potentially reduce Lyme disease risks.