Background & Summary

Borrelia burgdorferi sensu lato (B. burgdorferi s.l.), a complex of spirochete species that includes the causative agents of Lyme disease. As one of the most common vector-borne diseases in the temperate regions of the Northern Hemisphere1, Lyme disease can lead to severe neurological, cardiac, and joint complications, posing a significant threat to global public health2,3,4,5. This pathogen is mainly transmitted through tick bites6, so investigating the prevalence of B. burgdorferi s.l. in tick populations is of great significance for effective public health management.

The epidemiology of B. burgdorferi s.l. is inherently complex, influenced by various ecological factors including tick species diversity7, host populations8,9, and environmental conditions9,10,11,12. While regional studies have provided valuable insights into local infection dynamics13,14, the absence of a unified, continent-wide dataset severely limits our ability to identify broad-scale patterns and risk factors. Eurasia, as a special research area, has unique geographical and ecological characteristics. It has an extremely large geographical span, ranging from the cold-temperate forests in Northern Europe to the arid grasslands in Central Asia and the monsoon regions in East Asia, covering the most abundant types of ecosystems in the world. This uniqueness results in a highly diverse distribution of tick and host species in the region, and it is also a high-incidence area for Lyme disease. However, there is a significant imbalance in research in this region, with intensive studies in Western Europe, while data in regions such as Central Asia and Northern Asia are very scarce. Against this backdrop, previous studies have mostly focused on specific regions, using different methods and reporting standards, leading to heterogeneity in data collection and reporting15,16,17,18. This makes it difficult to conduct comparative analyses of the distribution patterns of B. burgdorferi s.l. and draw broader conclusions. Such fragmentation caused by inconsistent methodologies and limited geographical integration hinders a comprehensive understanding of the distribution of B. burgdorferi s.l. across Eurasia, resulting in a poor understanding of the differences in infection rates among different ecological regions, which in turn affects the prediction of Lyme disease risks and the identification of high-risk areas. Therefore, filling this knowledge gap through a standardized and coordinated dataset is crucial for enhancing disease prediction and formulating targeted public health strategies.

This study constructs a comprehensive dataset on the prevalence of B. burgdorferi s.l. in ticks across Eurasia by systematically integrating data from English and Chinese literatures from 2000 to 2023, providing the first continent-wide overview of the distribution of B. burgdorferi s.l. Our dataset encompasses all B. burgdorferi s.l. genospecies found in ticks, recognizing that while genospecies vary in their pathogenic potential for humans, comprehensive surveillance is essential for understanding Borrelia ecology and identifying emerging public health threats. This dataset can support reliable spatiotemporal analyses, quantify the differences in infection rates among different ecological regions, clarify the risk levels of different regions, help reveal the impact of climate change on the distribution of B. burgdorferi s.l., and provide strong support for understanding the long-term trends of disease transmission. This unified resource fills the gaps in existing data through integration, deepens our understanding of the ecology of B. burgdorferi s.l., and is of great significance for improving the risk assessment of Lyme disease and formulating targeted prevention strategies across Eurasia.

Methods

Literature Review

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines19,20, the literatures search, screening, and data extraction process are illustrated in Fig. 1. Potentially relevant publications were identified through systematic searches using the keywords “Tick* AND (Europe OR Asia OR country name) AND (Borreliosis OR “Borrelia burgdorferi”) AND (Abundance OR Prevalence)” in English databases (Web of Science, PubMed) and “伯氏疏螺旋体” in Chinese databases (CNKI, Wanfang, and VIP). The publication period was restricted to 2000–2023. No language restrictions were applied to the search.

Fig. 1
Fig. 1
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Literature search, screening, and data extraction process.

A total of 6141 publications were retrieved for screening, comprising 2516 from Web of Science, 2252 from PubMed, 477 from CNKI, 512 from Wanfang, and 384 from VIP databases. Duplicate publications were excluded during initial screening. During title and abstract review, we excluded non-research publications (reviews, editorials, books) and studies clearly unrelated to B. burgdorferi s.l.. In the full-text review stage, we excluded studies focusing on B. burgdorferi s.l. prevalence in hosts rather than ticks, laboratory studies without field data, method evaluation studies, infection efficiency trials, studies using preserved strains, publications with insufficient geographical information, and records with incomplete prevalence data. This resulted in 441 English and 81 Chinese publications meeting the extraction criteria.

Data collection

Key information extracted from the literature included: (1) tick occurrence location (and geographical scale), (2) the genera and species of ticks, (3) tick life and behavioral stages, (4) tick collection time and methods, and (5) prevalence of B. burgdorferi s.l.. A single publication often reported multiple tick species and their prevalence across different locations, which were recorded separately. Thus, each record in our dataset represents pathogen prevalence for a specific tick species reported by authors at a particular location.

Geographical positioning

The dataset includes the specific descriptions of locations provided in the literature. A hierarchical spatial scale approach was applied to classify locations by spatial resolution, with five levels established: country, and first-, second-, third-, and fourth-level administrative divisions. These administrative divisions at all levels were retrieved from GeoNames (https://download.geonames.org/), ensuring consistency in spatial classification standards. Each data record is precise to the smallest administrative division encompassing the sampling site or area.

For coordinate data, we applied a systematic approach based on the specificity of location information provided in the original publications. Sampling points with explicit coordinates were directly extracted from the texts, tables, figures, and supplementary materials. For locations lacking explicit coordinates but with clear and specific sampling point descriptions, accurate latitude and longitude were determined using the georeferencing function of Google Maps21,22, where relevant keywords for each record’s location—such as specific geographical features—were searched to obtain precise coordinates. For large sampling areas, ambiguous location descriptions, we used the center coordinates of the smallest administrative unit encompassing the sampling area to ensure consistent spatial representation.

Data Records

In the dataset of B. burgdorferi s.l. prevalence distribution in ticks across Eurasia (see figshare23), each row represents a single record (field surveillance data of B. burgdorferi s.l. prevalence in ticks reported by references at specific locations in particular years). In total, 2528 tick prevalence records were compiled across the Eurasian continent. The dataset columns are categorized into five major classes as shown in Table 1, namely Geographic locations, Tick characteristics, Sampling informations, Values, and Source.

Table 1 Description of dataset variables.
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Overall mean prevalence was 14.89% (95% CI: 14.27%–15.50%) across the Eurasian continent, with notable differences between Europe and Asia (Fig. 2). In Europe, the mean prevalence was 14.05% (95% CI: 13.38%–14.73%), with sampling locations predominantly concentrated in central and eastern regions. Particularly high prevalence clusters were observed in several central European locations. The Asian region exhibited a higher mean prevalence of 17.50% (95% CI: 16.10%–18.89%), although sampling locations were more dispersed compared to Europe. Notable clusters of high prevalence were identified in Eastern Asia, particularly in the northeastern regions.

Fig. 2
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Geographical distribution of B. burgdorferi s.l. prevalence across Eurasia.

Table 2 summarizes the prevalence of major Borrelia genospecies and tick genera across the Eurasian continent. B. burgdorferi s. l. showed substantial presence throughout the study region. Among specific genospecies, B. afzelii emerged as the most prevalent, followed by B. garinii and B. lusitaniae. In contrast, B. burgdorferi s. s. showed comparatively lower prevalence across Eurasia. The notable presence of co-infections highlights the complex ecological dynamics of Borrelia transmission, while the identification of untypeable Borrelia suggests potential uncharacterized genetic diversity within this pathogen complex. Among tick genera, Ixodes demonstrated the highest prevalence with the most extensive record coverage, followed by Haemaphysalis and Dermacentor. Hyalomma and Rhipicephalus showed lower prevalence rates with fewer records.

Table 2 Prevalence of major Borrelia genospecies and tick genera across Eurasia.
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Technical Validation

A systematic multi-stage validation process was implemented to ensure data integrity. Initially, a review team extracted the primary data, followed by comprehensive verification to eliminate redundancies and confirm accuracy. The georeferencing phase incorporated independent cross-validation by a separate researcher. To maintain consistency and reliability, standardized inclusion criteria were strictly adhered to by all team members throughout the data compilation process24.

Accurately annotating the geographic coordinates of Borrelia burgdorferi s. l. surveillance locations is critical for ensuring data validity, yet this posed significant challenges during compilation due to incomplete location descriptions in source literature—issues that often hindered positioning via the aforementioned geospatial tools. For instance, some locations were described using abbreviations or informal local identifiers (e.g., colloquial names of village ponds); others referred to extremely small-scale sites unrecognizable by online search services. Additionally, certain investigations focused on rivers or watersheds spanning multiple administrative regions, making it difficult to assign specific coordinates.

To address these spatial precision challenges, we implemented a systematic approach combining thorough examination of primary publications and supplementary materials with cross-referencing of semantic information. Given the varied geographical detail provided across studies, we developed a hierarchical classification system(levels 0–4) to systematically document the spatial precision of each record. This hierarchical framework ensures that records with the same classification level represent comparable spatial coverage scales, which is essential for subsequent spatial analyses. To enhance data transparency and facilitate user interpretation, we incorporated a “ Loc_descrip” column that captures the specific geographic descriptions mentioned in the original literature, providing users with comprehensive information about the exact sampling locations as reported by the authors. Our final dataset comprises 2528 records distributed across five spatial resolution levels: 170 records at the country level (loc_l0), 408 records at the first administrative level (loc_l1), 635 at the second level (loc_l2), 1140 at the third level (loc_l3), and 175 at the fourth level (loc_l4).

Usage Notes

The prevalence data of B. burgdorferi s.l. in ticks across Eurasia presented here represents the first comprehensive compilation spanning from 2000 to 2023. This dataset serves as a valuable resource for investigating both spatial patterns and, in future studies, temporal trends of B. burgdorferi s.l. infection rates. These data can support evidence-based decision-making for Lyme disease prevention and provide a foundation for time-series analyses of Borrelia prevalence.

The dataset can be utilized to analyze pathogen prevalence dynamics at multiple geographical scales and model the epidemiological risks of Lyme disease. It complements existing tick-borne pathogen surveillance datasets from North America, Europe, and other regions25,26, offering a unique focus on Eurasian territories, particularly with the integration of Chinese surveillance data.

Users should be aware of potential sampling biases in the literature compilation. Our search included Chinese-language databases (CNKI, Wanfang, and VIP), resulting in stronger representation of Chinese studies compared to other Asian regions. While European studies are well-represented through international databases, there is a comparative scarcity of studies from Central, South, and Southeast Asia. This reflects both our search strategy and the underlying distribution of published surveillance efforts. When conducting analyses across regions, users should consider these sampling differences, particularly when making inferences about areas with sparse data representation. In addition, users should note that the original studies employed various methods for B. burgdorferi s.l. detection, including PCR, Darkfield Microscopy, and RLB, which may introduce methodological heterogeneity. Additionally, tick species identification methods varied across studies, potentially affecting the accuracy of species-level analyses, especially for morphologically similar species. The dataset structure allows flexible filtering and aggregation to accommodate different research objectives. Potential applications include: epidemiological risk assessment, spatial pattern analysis, temporal trend evaluation, host-pathogen relationship studies, and public health policy development. We recommend users carefully consider sampling biases, methodological differences, and geographical coverage variations when conducting analyses.