The Role of Molecular Insights and Digital Innovation in Tick-borne Disease Research

Tick-borne diseases (TBDs) are emerging as a formidable public health concern globally, impacting both humans and animals through the transmission of various pathogens, such as bacteria (Borrelia, Rickettsia, Anaplasma), viruses (Tick-Borne Encephalitis virus, Crimean-Congo Hemorrhagic Fever virus), and protozoa (Babesia spp.). This intricate challenge stems from interactions between environmental stressors, pathogen evolution, host immune responses, and vector biology. Despite significant advancements, the complexities surrounding pathogen transmission and the host-vector-pathogen interplay necessitate deeper scientific exploration to enhance diagnosis, treatment, and public health responses.

Recent breakthroughs, including artificial intelligence (AI), machine learning (ML), and network modeling, alongside advanced "omics" technologies (genomics, proteomics, and metabolomics), have transformed our approach to studying TBDs. These innovations enable the construction of predictive models, uncover hidden molecular interactions, and facilitate the integration of vast datasets, profoundly impacting clinical decision-making and disease monitoring. However, a comprehensive understanding of the host-vector-pathogen triad remains elusive, underscoring the demand for interdisciplinary research that converges biological, immunological, ecological, and computational insights.

This Research Topic is associated with the 1st Annual Conference on Chronic Infection Pathologies. This collection aims to stimulate interdisciplinary contributions that offer new perspectives on the biological, immunological, ecological, and computational facets of tick-borne diseases. Highlighting the integration of traditional and data-driven methodologies, we encourage submissions from diverse fields, spanning experimental, translational, and in silico research, to enhance our holistic understanding. To gather further insights into tick-borne disease science, we welcome articles addressing, but not limited to, the following themes:

- Multi-omics analyses of tick, pathogen, and host systems
- Host-pathogen immune interactions and immune evasion strategies
- AI/ML applications in biomarker discovery, diagnostics, and disease risk prediction
- Vector competence, microbiome studies, and tick genetics
- Coinfections and polymicrobial interactions in TBDs
- Modeling pathogen spread, host susceptibility, and climate impacts
- Digital epidemiology and One Health data integration frameworks
- Novel diagnostic, therapeutic, and vaccine development pipelines

We invite cutting-edge research that surpasses conventional vector biology, merging systems-level thinking with innovative data science approaches and offering valuable insights for public health management, clinical care, and future epidemic preparedness. This collection seeks to be a nexus for pioneering research at the intersections of immunology, digital health, and tick-borne disease biology by blending wet lab research with advanced analytics.

Topic Editor Leona Gilbert is a co-founder and a shareholder of Te?ted Oy. All other Topic Editors declare no competing interests with regard to the Research Topic subject.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review

Keywords: Tick-borne diseases, TBDs, AI, machine learning, ML, One Health

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.