Skip to main content
Entomology 2021 Main banner
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.
Presentation Icons
In-Person
In-Person
Livestream
Livestream
On-Demand
On-Demand
Student Competition
Student Competition
Video Chat
Video Chat
Ticketed Event
Ticketed Event
Poster Icons
Student Competition
Student Competition
In-Person
In-Person
Poster
Poster
On-Demand
On-Demand
Back
Video Q&A ViewAttendees 2

Medical, Urban, and Veterinary Entomology

10-Minute Paper

MUVE: Tick Ecology and Control On-Demand Presentations

Identifying suitable habitat for Borrelia burgdorferi infected Ixodes scapularis nymphs in the eastern United States and alignment with the geographic distribution of Lyme disease cases

On-Demand
On-Demand

Lyme disease, caused primarily by Borrelia burgdorferi sensu stricto, is the most commonly reported vector-borne disease in the United States The geographic areas where human illness occurs and where the primary vector, Ixodes scapularis, is established are both expanding. Field surveillance efforts for ticks and their associated pathogens has increased in recent years. The resulting information on the distribution, density, and associated infection prevalence of ticks and their pathogens is intended to provide current and accurate information regarding geographic risk for human exposure to ticks and tickborne pathogens. We used tick distribution and B. burgdorferi infection data from 2696 counties in the eastern US reported to the Centers for Disease Control and Prevention (CDC) through ArboNET, combined with historical data, to develop ensemble models. We identified counties with suitable habitat for risk of exposure to host-seeking B. burgdorferi s.s.-infected I. scapularis nymphs. Predictions from these models were compared with the distribution of Lyme disease cases reported to CDC. Environmental factors affecting predictions for counties with higher vs. lower acarological risk, comparisons of model outcomes with epidemiological data, and how the models can be used to identify counties where enhanced tick surveillance might be justified will be discussed.