Zoonotic diseases—those transmitted between animals and humans—represent a growing threat to global health, economies, and ecosystems. Traditional surveillance methods often struggle to keep pace with the speed and complexity of emerging zoonotic threats, especially in under-resourced or ecologically dynamic settings. Recent advances in artificial intelligence, remote sensing, environmental biosensors, and geospatial modeling have opened the door to more precise, real-time detection and mapping of zoonotic risk. These tools offer the potential to identify outbreaks earlier, pinpoint hotspots, and improve targeted interventions at the human-animal-environment interface. Despite growing interest in these technologies, the integration of precision tools into operational surveillance systems remains fragmented and underdeveloped. This Research Topic aims to explore and advance the use of next-generation surveillance technologies to transform how we detect, monitor, and respond to zoonotic diseases across veterinary and public health systems.

he early detection of zoonotic diseases is critical to preventing outbreaks and minimizing their impact on animal and human health. However, traditional surveillance systems are often reactive, geographically limited, and constrained by delays in reporting or diagnostic confirmation. As zoonotic threats grow in frequency and complexity, there is an urgent need to shift from passive observation to proactive, precision-based surveillance. This Research Topic seeks to address the challenge of early and accurate detection by highlighting emerging tools and strategies that enhance our ability to monitor zoonotic disease risk.

We aim to showcase advances in technologies such as artificial intelligence, machine learning, environmental biosensors, genomic surveillance, and geospatial mapping that allow for real-time data integration and hotspot prediction. We also welcome submissions focused on field-deployable diagnostics, participatory surveillance, and innovative data-sharing platforms. By bridging veterinary science, data science, and public health, this Research Topic will provide a multidisciplinary foundation for improving the accuracy, timeliness, and scalability of zoonotic disease surveillance systems around the world.

This Research Topic invites submissions that examine innovative tools, platforms, and strategies aimed at enhancing the precision and responsiveness of zoonotic disease surveillance. We are particularly interested in studies that explore the use of artificial intelligence and machine learning for predictive modeling, the application of geospatial and remote sensing technologies to identify high-risk areas, and the development of environmental biosensors and rapid field diagnostics. Manuscripts addressing genomic surveillance, participatory surveillance approaches, and the integration of data across veterinary, environmental, and human health systems are also encouraged.
We welcome a range of manuscript types, including original research articles, reviews, case studies, and perspectives. Contributions should focus on novel approaches or technologies that improve early detection, monitoring, and decision-making in the context of zoonotic disease threats. Interdisciplinary and collaborative research that bridges gaps across One Health domains is especially encouraged.

Topic Editor Richard J Obiso provides consulting for private company HueDx (www.huedx.com). No other competing interests have been declared by the Topic Editors with regards to the subject of this Research Topic.

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Precision Surveillance of Zoonotic Diseases: Innovations in Early Detection and Mapping