Microbiomes are essential components of ecosystems and human health, acting as mediators of ecological balance and biological functions. Recent technological advancements in sequencing, computational modeling, and bioinformatics have made it possible to observe shifts within microbial communities with remarkable detail. These shifts can potentially serve as early warning signals, indicating possible threats such as disease outbreaks or antimicrobial resistance. However, a major challenge lies in deciphering which variations are benign and which signal critical transitions. The integration of longitudinal data, risk assessment frameworks, and predictive models are fundamental in developing dependable microbiome surveillance systems that can differentiate these variations effectively.

This Research Topic aims to investigate how changes in microbiomes can serve as preliminary indicators of potential threats to human, animal, and environmental health. It seeks to advance methodologies for detecting and leveraging these shifts to facilitate proactive interventions. Despite the growing understanding of microbiome significance, we lack a solid framework to differentiate routine fluctuations from significant warning signals. Recent developments in high-throughput sequencing, machine learning, and time-series analysis have emerged, offering new opportunities to identify subtle microbial changes before they lead to major health or environmental challenges. The realization of these potentials depends heavily on interdisciplinary collaboration spanning fields such as bioinformatics, microbiology, medicine, epidemiology, veterinary science, and public health. This Research Topic spotlights innovative tools, real-world applications, and conceptual frameworks aiming to transition from descriptive studies to actionable surveillance strategies that can predict and forestall emergent risks.

To gather further insights into the range and limitations of microbiome surveillance, we welcome articles addressing, but not limited to, the following themes:

-Advances in microbiome surveillance across diverse ecosystems, including human health, agriculture, clinical environments, urban settings (e.g., cities, wastewater systems), and natural habitats.
-Frameworks for identifying early indicators of diseases, outbreaks or ecosystem disturbances amidst normal microbiome variability.
-Computational approaches, such as machine learning and time-series analysis, for the early detection of microbial shifts.
-Microbiome indicators relevant to bacterial infections, foodborne pathogens, antimicrobial resistance, or environmental imbalances.
-Integration of microbiome data within One Health strategies for enhancing diagnostics, disease monitoring, and public health interventions.
-Applied research: real-world implementations of microbiome early warning systems.

We invite diverse submission formats including original research articles, reviews, and other types to contribute to this expanding field.

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Microbiome Surveillance: Emerging Health and Environmental Risks