Study unravels relationship between bacteria and environment in Lyme disease ecology

Predicting Lyme disease hotspots can help public health officials direct resources and send proactive messages to the public. But the ecology of the disease is complex, involving various animal hosts, the blacklegged ticks that serve as the vector of the disease, the pathogen itself, the bacterium Borrelia burgdorferiand the environment in which they all live.

The study, published in the Journal of Applied Ecology, unravels the relationship between two of these actors in the ecology of Lyme disease: bacteria and the environment. Led by Tam Tran, who earned her PhD in Penn’s Department of Biology in the School of Arts & Sciences, and with mentors Dustin Brisson, Department Professor, Shane Jensen of the Wharton School, and state colleagues from the New York Department of Health, the research investigates how variables such as landscape disturbance and climate affect the distribution and abundance of 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 help mitigate disease transmission.

“We know that Lyme disease is a growing threat to public health, but we haven’t found great ways to deal with it. The number of cases just keeps going up,” says Tran, now a college student in medicine at Virginia Commonwealth University. “What’s exciting here 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 amounts of pathogens in the landscape. .”

In the present study, Tran, Brisson, Jensen and their colleagues focused primarily on factors that influenced B. burgdorferi, whose prevalence they measured by determining what fraction of blacklegged ticks they collected were infected with the bacteria. Older attempts to draw links between Lyme disease and environmental variables have had mixed, unclear, or sometimes even contradictory results, Tran says, in part because contributions from the broader “environment” can be so multifaceted.

To build their models, the research team took data collected from nearly 19,000 blacklegged ticks between 2009 and 2018 at hundreds of sites in New York State. They assessed how the numbers of infected and uninfected ticks in hundreds of locations over more than a decade aligned with local environmental characteristics falling into four broad categories:

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

2) population size of vertebrate hosts, including humans, bears, birds, and deer;

3) monitoring conditions, including local temperature and humidity at the time of collection as well as effort expended in collecting specimens; and

4) climatic measures such as monthly averages of temperature, precipitation and days with temperatures below freezing.

By running various groupings of these variables through powerful computer models, the researchers were able to determine which were most influential in determining infectiousness rates.

“The main finding was that climate was an overwhelming feature of the model,” says Tran. “Habitat disturbance was also significant, and we found the opposite of what has emerged from previous studies in some cases.”

While previous analyzes had shown that increased disturbance – things like fires, roads that cut through forests and fragmented swaths of habitat – lead to increased B. burgdorferi numbers, the Penn-led team found that less disturbed and more intact habitats were often associated with higher numbers of ticks infected with the bacteria.

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

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

Interventions could be public health messages warning park visitors of, say, disease risk, “reminding them to do their tick checks,” Tran says. The findings could also help guide future land management, harnessing the power of ecology to potentially reduce Lyme disease risk.


University of Pennsylvania

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