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<p>One Health is a transdisciplinary framework that recognizes the interdependence of human, animal and environmental health. Here we propose an integrated sensing strategy guided by this approach to continuously track biological and environmental signals across humans, animals and their shared environments. We identify key transmission pathways through which pathogens and toxins move between environmental reservoirs, animal hosts and human populations and discuss how sensing modalities could be linked to support cross-sector surveillance. Extending wearable and on-body sensing approaches to environmental, infrastructural and animal surfaces would enable coordinated, real-time monitoring of emerging biological and chemical threats, thereby supporting early outbreak detection and informing global health responses. We further highlight that artificial-intelligence-enabled fusion of multi-sensor data can transform these systems into a closed-loop architecture supporting early detection, risk assessment and informed intervention for epidemics and zoonotic threats.</p>

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Integrated sensing networks for One Health monitoring

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Shichao Ding,
  • Chuanrui Chen,
  • Selene Tang,
  • Shengqi Wu,
  • Zhengxing Li,
  • Tamoghna Saha,
  • Joseph Wang

摘要

One Health is a transdisciplinary framework that recognizes the interdependence of human, animal and environmental health. Here we propose an integrated sensing strategy guided by this approach to continuously track biological and environmental signals across humans, animals and their shared environments. We identify key transmission pathways through which pathogens and toxins move between environmental reservoirs, animal hosts and human populations and discuss how sensing modalities could be linked to support cross-sector surveillance. Extending wearable and on-body sensing approaches to environmental, infrastructural and animal surfaces would enable coordinated, real-time monitoring of emerging biological and chemical threats, thereby supporting early outbreak detection and informing global health responses. We further highlight that artificial-intelligence-enabled fusion of multi-sensor data can transform these systems into a closed-loop architecture supporting early detection, risk assessment and informed intervention for epidemics and zoonotic threats.