Data-Driven Integration of Technologies in Food Biosecurity and Antimicrobial Resistance: A One Health Perspective

About this Research Topic

Background

Antimicrobial resistance (AMR) and food biosecurity are pressing global concerns, intertwined within the broader context of human, animal, and environmental health. Food systems are particularly vulnerable to these challenges, requiring innovative solutions to ensure safety and sustainability. Recent advancements in data-driven techniques, such as AI and predictive modeling, coupled with laboratory-based methods including microbial genomics and environmental pathogen detection, have enriched our toolkit for tackling these issues.
However, the full potential of these advancements is yet to be realized, with ongoing debates on effectively integrating computational and experimental methodologies to address food safety from a One Health perspective.
Collaborative efforts among different disciplines are essential to develop comprehensive strategies that mitigate AMR risks and safeguard food biosecurity.

This Research Topic aims to enhance both AMR mitigation and food biosecurity by leveraging data-driven and molecular approaches within the One Health framework. The main objectives are to foster interdisciplinary collaborations that translate computational and experimental insights into practical strategies for food safety and AMR containment. Emphasizing synergies between technological innovations and sustainable practices, this topic seeks to drive advancements that protect food systems and promote public health resilience against microbial threats.

To gather further insights in harmonizing considerations for both food biosecurity and AMR, we welcome articles addressing, but not limited to, the following themes:

o Data-driven innovations: AI and machine learning for predicting resistance patterns and optimizing biosecurity measures across food supply chains
o Laboratory and clinical analyses: Development of rapid diagnostics for foodborne pathogens and evaluation of biosecurity protocols
o Integrated applications: One Health risk assessments that combine environmental monitoring with food safety data, and studies that bridge experimental findings with computational models
o Translational strategies: Application of analytical methods to strengthen AMR surveillance and food safety policies globally
o Cross-sectoral collaborations: Interdisciplinary efforts to enhance AMR mitigation and biosecurity interventions, reinforcing food systems' resilience

We invite submissions that demonstrate how diverse analytical methodologies—computational, experimental, or clinical—can effectively enhance food biosecurity and combat AMR through a cohesive One Health approach. These insights will be pivotal in curbing evolving microbial threats and ensuring equitable, science-based solutions for food safety.

Article types and fees

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

• Editorial
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• ... 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:

• Editorial
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: AMR, food biosecurity, biosecurity, data analysis, predictive modeling, artificial intelligence, Food safety, Foodborne pathogens, 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.

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