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Bayesian Multivariate Modelling of Lone Star (Amblyomma americanum) Tick Life Stage Abundance and Temporal Trends to Inform Public Health Risk in Virginia
Sensors
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- Date Published
- 15 May 2026
- Priority Score
- 0
- Australian
- No
- Created
- 15 May 2026, 12:00 pm
Authors (4)
Description
International Journal of Environmental Research and Public Health, an international, peer-reviewed Open Access journal.
Summary
This study utilizes Bayesian multivariate modelling to analyze the seasonal patterns and habitat preferences of the Lone Star tick in Virginia. While the research employs advanced statistical modelling techniques to track biological life stages, it does not address artificial intelligence safety, governance, or catastrophic risks. The focus remains exclusively on public health surveillance and ecological risk assessment related to tick-borne diseases. Consequently, it offers no contribution to the discourse on frontier AI capabilities or global AI policy frameworks.
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Open AccessArticle
Bayesian Multivariate Modelling of Lone Star (Amblyomma americanum) Tick Life Stage Abundance and Temporal Trends to Inform Public Health Risk in Virginia
by
Thabo Lephoto, Henry Mwambi, Oliver Bodhlyera and Holly Gaff
Int. J. Environ. Res. Public Health 2026, 23(5), 660; https://doi.org/10.3390/ijerph23050660 (registering DOI) - 15 May 2026
Abstract
The increasing abundance of ticks poses a growing public health concern due to heightened human exposure to tick bites. The lone star tick (Amblyomma americanum: Ixodida: Ixodidae), a common human-biting species in the United States, has expanded its range in recent
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The increasing abundance of ticks poses a growing public health concern due to heightened human exposure to tick bites. The lone star tick (Amblyomma americanum: Ixodida: Ixodidae), a common human-biting species in the United States, has expanded its range in recent years. However, how its different life stages vary across time, habitats, and locations remains insufficiently understood. We analyzed tick abundance data collected in southeastern Virginia between 2009 and 2018, focusing on larval, nymphal, and adult life stages. A Bayesian multivariate modelling framework was used to examine seasonal patterns, habitat effects, spatial variation, and biological links between life stages. Two commonly used count models were compared to determine which best described the observed tick abundance patterns. Tick abundance showed strong and distinct seasonal patterns across life stages. Adult ticks were most abundant in late spring to early summer (May–June), nymphs peaked in early to mid-summer (June–July), and larvae peaked later in summer (August). Wooded habitats consistently supported higher tick abundance than grassy areas. Although both models captured these trends, the negative binomial model provided a more stable and biologically meaningful representation of tick dynamics. Several counties, including Chesapeake, York, Portsmouth, and Northampton, were identified as areas of elevated tick abundance, indicating increased tick bite exposure risk. This study highlights clear seasonal and habitat-specific windows of increased tick activity that are relevant for surveillance and control planning. By clarifying when and where different tick life stages are more abundant, the findings support targeted public health interventions aimed at reducing human exposure to tick bites in Virginia. The modelling approach is also applicable to other regions, including settings where ticks affect livestock health and food security.
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