ArticleParasites & vectors2025
Variables for habitat and vertebrate hosts of Ixodes scapularis are the best ecological predictors of the spatial spread of Lyme disease in the United States (2010-2019).
Article in Parasites & vectors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Evaluation of EcoCRM, virus-like particles, and mRNA as vaccine platforms against Borrelia burgdorferi.Vaccine · 2026Article
- Contrasting epigenetics of Ixodes scapularis populations.Scientific reports · 2026Article
- Scientist's Opinion on Climate Change and Hard Ticks (Ixodidae).Pathogens (Basel, Switzerland) · 2026Article
- Sixty years of climate impact on Ixodes ricinus (Ixodidae).Experimental & applied acarology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLyme disease (LD) is a major public health concern in North America. The incidence of LD has increased in part due to the rapid expansion of Ixodes scapularis infected with Borrelia burgdorferi sensu lato (Bb), the causative agent of LD. Understanding how environmental factors contribute to the spread of LD in humans remains a major challenge.
methodsWe aimed to measure the environmental associations and spatial dynamics of LD incidence across United States counties between 2010 and 2019 via a machine-learning (ML)-based model. We used LD incidence data in 1322 counties in 24 US states from the Centers for Disease Control and Prevention (CDC), categorized by four incidence classes (0-1, > 1 to 10, > 10 to 100, > 100 cases/100,000 population). Explanatory variables of climate, habitat, land cover, I. scapularis presence, and distribution of tick hosts and Bb reservoirs were used to train the ML models.
resultsThe performance of a random forest algorithm was high (area under the curve [AUC] = 0.89). As expected, surveillance-dependent variables for adjacency to LD-endemic counties (gain ratio: 0.22) and presence of I. scapularis (gain ratio: 0.133) were identified as the top individual predictors of LD spread. However, the strongest overall contributions to the model were driven by vertebrate-related variables (n = 8) (ReliefF: 0.237), with landscape features for forest growth, canopy, and forest edge (length) also identified as strong (gain ratio > 0.28) individual predictors. Climate predictors indicated the lowest LD incidence classes (< 10 cases/100,000 population) in warmer and drier counties and the highest LD incidence classes (> 10 cases/100,000 population) in the coldest and wettest counties.
conclusionsUtilization of ML algorithms trained with variables impacting the circulation of Bb produced a comprehensive model of county-level LD incidence and captured the main factors acting on the spread of the pathogen. This represents an important step towards an integrated framework aimed at capturing LD incidence changes for preventive purposes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.