Standardizing Global Public Health: PHES-ODM Version 3 and the Future of Wastewater Surveillance

      Wastewater surveillance (WWS) has rapidly evolved into an indispensable tool for public health. Its utility, particularly highlighted during the COVID-19 pandemic, lies in its ability to detect pathogens and track population-level health trends without direct individual testing. This allows for early warning systems and broad public health insights, offering a cost-effective and non-invasive monitoring method. However, the true long-term potential of WWS has been hampered by significant challenges: fragmented data systems, inconsistent metadata practices, and a critical lack of interoperability across different programs and institutions.

      The Public Health and Environmental Surveillance Open Data Model (PHES-ODM) was developed precisely to address these pressing issues. PHES-ODM provides an open, collaborative framework designed to standardize WWS data, ensuring transparent and ethical data use that aligns with the globally recognized FAIR principles. From its origins in Ottawa, Canada, during the early days of SARS-CoV-2 WWS, PHES-ODM has grown in adoption, now utilized in over 25 countries and adapted by major entities such as the Public Health Agency of Canada and the EU Sewage Sentinel System. This article introduces Version 3 of the PHES-ODM, a significant advancement poised to overcome persistent barriers to data utility and seamless interoperability.

The Critical Need for Standardized Wastewater Surveillance Data

      The swift expansion of WWS programs globally, with hundreds of universities and thousands of sites engaged in over 70 countries, underscored its value. Organizations like the World Health Organization, the Rockefeller Foundation, and the Bill and Melinda Gates Foundation have recognized WWS as a vital addition to public health pathogen surveillance. Despite this rapid adoption, a core challenge remains: fragmented data. Without standardized data, large-scale coordination, comprehensive meta-analytical research, and the integration of wastewater insights into actionable public health policy become incredibly difficult. This restricted access and inconsistent formatting often mean valuable data is underutilized or even wasted.

      The absence of universally accepted data standards, coupled with varying levels of system literacy and diverse fields involved in WWS, creates significant hurdles. Issues like inconsistent data reporting, non-interoperable data formats, and insufficient metadata collection directly threaten the longevity and utility of WWS programs. To ensure public funds are used effectively and public trust is maintained, collected data must be understandable, analyzable, and ethically managed. ARSA Technology, for instance, understands these challenges, providing custom AI solutions that are designed with data integrity and interoperability at their core, ensuring that diverse data sources can be harmonized for maximum impact.

Introducing PHES-ODM Version 3: A Leap in Data Interoperability

      PHES-ODM was conceived as a common language for discussing and exchanging WWS data globally. It fills a critical void for a structured, openly available system to record and store WWS data. Transparency in data management is fundamental to its ethical handling, and metadata – essentially "data about data" – is crucial for providing context and preventing misinterpretation. This is especially vital when dealing with human health data, where misinterpretation can lead to severe consequences. PHES-ODM ensures that data is not only collected but also contextualized, making it truly useful and reusable.

      Version 3 of the PHES-ODM builds on this foundation by introducing key enhancements that significantly improve data interoperability and utility (Thomson et al., A PREPRINT, arXiv:2604.18762v1 [cs.DB] 20 Apr 2026). As a relational data model, it structures information in a way that links various entities—such as sampling sites, collected samples, specific measurements, and affected populations—using unique identifiers. This robust framework ensures that the entire analytical lifecycle of the data is interconnected, providing a comprehensive view from collection to public health action. This level of structured data management is essential for advanced analytical approaches, much like the precision ARSA aims for in its AI Video Analytics systems, which convert raw video streams into actionable intelligence for various operational needs.

Key Enhancements for Robust Public Health Intelligence

      The latest iteration of PHES-ODM introduces several critical features designed to overcome the "persistent barriers to interoperability and data utility." These enhancements make the model more adaptable and powerful for diverse surveillance programs:

• Public Health Actions: New tables allow for the documentation and linkage of specific public health responses to surveillance data. This means WWS data can now be directly tied to interventions, providing a clearer picture of impact and enabling better evaluation of strategies.
• External Repository Linkages: The model now supports connections to external biological data repositories such as GISAID (for viral genomic data) and GenBank. This integration enriches WWS data with genomic context, enabling more precise pathogen tracking and evolutionary analysis.
• Analytical Workflow Documentation: PHES-ODM v3 includes enhanced capabilities for documenting the entire analytical workflow. This transparency ensures that the methods used to process and interpret data are clearly recorded, improving trust, reproducibility, and the ethical use of information.
• Complex Relational Linkages: The model’s relational structure has been further optimized to support intricate connections across different data entities. This means a single sample can be linked to multiple sites or populations, and multiple measures can be associated with a single sample, offering unprecedented flexibility and depth in analysis.
• Data Format Mapping Tools: To facilitate broader adoption and integration, PHES-ODM v3 introduces tools for mapping data across other existing WWS standards, including the US CDC National Wastewater Surveillance System (NWSS) and PHA4GE. This significantly reduces the burden of data transformation, enabling seamless data exchange between different systems.
• Support for Diverse Data Formats: The model is now equipped to handle both "long" and "wide" data formats, accommodating various data collection and reporting preferences without compromising standardization.

      These enhancements translate directly into tangible benefits for public health organizations. By providing clearer, more comprehensive, and readily accessible insights, decision-makers can react faster and more effectively to emerging health threats. This robust infrastructure reduces the risk of misinterpretation, increases operational efficiency, and ultimately contributes to better public health outcomes.

Achieving Data Justice and Trust with FAIR Principles

      At its core, PHES-ODM is built upon the FAIR Data Principles: data must be Findable, Accessible, Interoperable, and Reusable. These principles are not just technical guidelines; they are foundational to data justice and maintaining public trust. Without context, data can be easily misinterpreted, leading to flawed decisions. By providing a comprehensive data dictionary that standardizes definitions and ensures rich metadata collection, PHES-ODM supports transparent data curation throughout its entire lifecycle.

      The model's approach emulates successful precedents in public health, such as LOINC (Logical Observation Identifiers Names and Codes), a universal standard for medical laboratory measures. This commitment to standardization and ethical data management is crucial for the advancement of any field, especially emerging areas like WWS and wastewater-based epidemiology (WBE), where collaboration across diverse practices and institutions is essential. ARSA, for example, prioritizes robust data management and privacy, especially with solutions like the Self-Check Health Kiosk, which handles sensitive personal health information with GDPR/HIPAA encryption, demonstrating alignment with the principles PHES-ODM champions.

Leveraging Open Data Models for Enterprise AI & IoT Solutions

      The advancements in PHES-ODM v3 highlight a broader trend in leveraging structured data for impactful outcomes across various sectors. While specifically for wastewater, the principles of standardized, interoperable, and securely managed data are fundamental to any effective surveillance or intelligence system. Enterprises and public institutions can significantly benefit from adopting such open data models. They reduce data fragmentation, enhance analytical capabilities, and ensure regulatory compliance, which is critical for operations where data accuracy and privacy are paramount.

      ARSA Technology, an AI & IoT solutions provider experienced since 2018, specializes in designing, building, and deploying production-ready systems that integrate complex data into actionable intelligence. Our expertise spans computer vision, industrial IoT, and custom software engineering, enabling us to help organizations implement solutions that adhere to stringent data standards and interoperability frameworks. Whether it’s optimizing public safety through advanced AI video analytics, implementing smart infrastructure, or developing custom platforms that consume and produce standardized environmental health data, ARSA delivers reliable and scalable solutions built to generate measurable financial and operational outcomes.

      The Public Health and Environmental Surveillance Open Data Model Version 3 represents a significant step forward in making wastewater surveillance data more robust, reliable, and actionable. By providing a scalable, modular, and open infrastructure, it empowers public health authorities and environmental programs to extract maximum value from their data, ensuring better coordination, informed decision-making, and ultimately, improved public health outcomes globally.

      To explore how ARSA Technology can help your organization leverage advanced AI and IoT solutions with robust data management for critical operations, we invite you to contact ARSA for a free consultation.