Insights Around Incidence Rates and Evolving Diagnostics for Vector-Borne Diseases

The incidence rate of Lyme disease in the US continues to be significant. According to the Centers for Disease Control and Prevention (CDC), over 89,000 cases of Lyme disease were reported to the federal agency by state health departments and the District of Columbia in 2023. The CDC also says estimates run as high as 476,000 people being diagnosed and treated for Lyme disease annually, but this number likely includes patients who are treated based on clinical suspicion but do not actually have Lyme disease.¹

“As far as tick-borne, I think we're going to see Lyme disease remain our top tick transmitted infection for some time,” said Kyle G. Rodino, PhD, D(ABMM), assistant professor of Clinical Pathology and Laboratory Medicine at the Perelman School of Medicine at the University of Pennsylvania. He says people’s outdoor activities will continue to drive those numbers.

And while Lyme disease continues to see the greatest number of cases annually, the growth of other vector-borne diseases, especially mosquito-borne illnesses, is something Rodino says interested stakeholders are watching.

“When we talk about mosquito-borne illnesses, particularly where those mosquitoes can end up being everywhere, people are exposed to them all of the time,” Rodino said.

Next Generation of Diagnostics, AI-Integration

Diagnosing vector-borne diseases and illnesses can be challenging, especially as the clinical presentation and diagnostics may allow these illnesses to go undiagnosed. For example, the diagnostic process is still a challenge for Lyme disease. It is primarily done through clinical evaluation, with consideration of symptoms like the "bullseye" rash, exposure to tick-prone areas, and physical exams. Although blood tests, such as the ELISA and Western Blot, are used, they can show negative results in early infection, and treatment is often initiated based on symptoms alone before tests turn positive.²

The good news is there are some newer diagnostics, such as metagenomic next-generation sequencing (mNGS), as well as AI-assisted microscopy.

Rodino says there are a few potential benefits to mNGS. “It's agnostic. So theoretically, if there's a tick-borne pathogen in a sample, it can detect it. So it does cast that broad net that if you find yourself ordering a large number of individual tests, maybe something bundled like this is really potentially a great future solution…I think the other thing is that I bet we're not done discovering the vector borne illnesses that are around, particularly the viruses…There's things that probably that we just have not had tests for, that would explain an otherwise undetermined, febrile summer illness that are probably vector-borne infections. And I think the idea of using metagenomics to detect those novel or rare organisms is very interesting. And then lastly, it overcomes some of those patient factor issues—if you don't know someone's immune status and you want to, it can really detect anything that's in the sample, so it theoretically can overcome that challenge as well.”

In terms of challenges with mNGS, it is a direct detection method, explains Rodino. “If the patient is presenting with an arbovirus, and that virus has already cleared, and most of what they're experiencing is a post inflammatory syndrome type thing, this very fancy method is not going to overcome the fact that viral nucleic acid is not present so it has technical limitations.”

Rodino says AI-assisted microscopy is emerging as a powerful complement to traditional diagnostic methods, particularly in the detection of parasitic infections like babesiosis. While manual microscopy remains a gold standard, maintaining proficiency has become increasingly difficult. In many clinical settings—especially those with lower case volumes—laboratorians may encounter only a handful of positive cases each year, making it challenging to sustain the level of expertise required for accurate identification. This decline in routine exposure, combined with the inherently time-intensive nature of reviewing blood films, creates a bottleneck in diagnostic workflows.

The integration of AI into microscopy offers a practical solution to these challenges by dramatically improving efficiency and throughput. AI systems can rapidly scan and filter large volumes of slides, identifying those that are most likely to contain pathogens.

“AI assistance can really help us—it can likely filter through and screen dramatically more slides than an individual technologist can do by hand,” Rodino said. By eliminating the need to manually review the majority of negative samples, AI enables laboratorians to focus their attention on cases that truly require expert interpretation. This not only accelerates diagnosis but also ensures that human expertise is applied where it matters most.

Although it remains to be seen when these newer diagnostics and AI integration will come online for most clinicians, person-to-person communication with the lab and their clinical peers remains paramount.

“I would stress and encourage people that they need to get out of their comfort zones and talk to people about the optimal ways to diagnose tick-borne diseases…I think it's the old school. Know your neighbor and what challenges they're facing, and see if you can help alleviate some of them,” Rodino said.

To learn more about diagnostics for vector-borne diseases, read Rodino’s paper on the subject here.

References
1. Lyme Disease Surveillance and Data. CDC. March 15, 2025. Accessed April 6, 2026.
https://www.cdc.gov/lyme/data-research/facts-stats/index.html
2. Testing and Diagnosis for Lyme disease. CDC. May 15, 2024. Accessed April 6, 2026.
https://www.cdc.gov/lyme/diagnosis-testing/index.html