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Updated: May 24, 2025
Detecting the Lyme Disease Spirochete, Borrelia Burgdorferi, in Ticks Using Nested PCR
Published on: February 4, 2018
Active and Passive Vector Surveillance Systems in Lyme Endemic Minnesota and Their Correlation to Human Disease
Kathleen E Angell1, Janet Jarnefeld, Elizabeth K Schiffman
1Author Affiliations: Department of Epidemiology, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska (Ms Angell, Dr Degarege, and Dr Brett-Major); Metropolitan Mosquito Control District, Saint Paul, Minnesota (Ms Jarnefeld); Minnesota Department of Health, St. Paul, Minnesota (Ms Schiffman); Global Center for Health Security, University of Nebraska Medical Center, Omaha, Nebraska (Drs Broadhurst and Brett-Major); Department of Pathology, Microbiology, and Immunology, College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska (Dr Broadhurst); Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska (Dr Dong); and School of Veterinary Medicine and Biomedical Sciences, University of Nebraska, Lincoln, Nebraska (Dr Cortinas).
Context:
Vector surveillance is often used to predict tick-borne diseases in endemic regions. Active and passive vector surveillance systems offer differing benefits and limitations; understanding how the outputs of these systems differ and how they correlate to human disease is essential to public health decision-making. Active and passive vector surveillance systems in Minnesota between 2018 and 2023 present an opportunity for comparison between these surveillance methods.
Objective:
To (1) analyze, compare, and contrast the results of active vector surveillance with crowd-sourced approaches, and (2) explore how these sources predict risk of Lyme disease.
Methods:
In this ecological comparative analysis, descriptive statistics were performed to evaluate characteristics of each surveillance method to assess differences in seasonality, life stage, and species of ticks. Negative binomial regression was used to analyze correlation to Lyme disease.
Results:
There are differences between data sources in tick life stage, species, and seasonality. Active surveillance using small mammal trapping had a majority larval (85%) and I. scapularis (76%) ticks. In contrast, passive surveillance had a majority of adult (96%) and D. variabilis (75%) ticks. Observations in both data sources were skewed to the early third of the tick season, although this was more exaggerated in the passive surveillance data. Observations of ticks from both data sources positively correlated with cases of Lyme disease.
Conclusions:
Observed differences in tick characteristics between the 2 data sources may represent real differences between tick populations and human encounters. Some differences may be explained by observation, reporting, and sampling biases. Increased observations of ticks at the beginning of the season indicate potential utility of enhanced human Lyme disease surveillance at that time. These One Health findings signal an opportunity for early identification of high tick-borne disease years through integrated active and passive tick surveillance that informs the conduct of human disease surveillance.
