Douglas Manuel

Mathew Thomson, Jean-David Therrien, Nikho Hizon et al.

Wastewater surveillance (WWS) has emerged as a valuable tool for public health surveillance, particularly since the COVID-19 pandemic. Its long-term utility is constrained, however, by fragmented data systems, inconsistent metadata practices, and poor interoperability. The Public Health and Environmental Surveillance Open Data Model (PHES-ODM) was developed as an open, collaborative framework to standardize WWS data and support transparent, ethical data use aligned with FAIR principles. Adopted by the Public Health Agency of Canada and adapted by the EU Sewage Sentinel System, the model is now used in over 25 countries. This paper introduces version 3 of the model, which addresses persistent barriers to interoperability and data utility. Key enhancements include new tables for public health actions, external repository linkages (e.g., GISAID, GenBank), and analytical workflow documentation, as well as support for complex relational linkages across sites, samples, measures, and populations. Tools for mapping across other data formats, including PHA4GE and the US CDC National Wastewater Surveillance System, and for supporting long and wide data formats are also introduced. We compare PHES-ODM against six other WWS data standards across 25 features. Balancing robustness with usability, PHES-ODM v3 provides a scalable, modular infrastructure adaptable to diverse WWS and environmental surveillance programs.

Arya Rahgozar, Pouria Mortezaagha, Jodi Edwards et al.

The Brain-Heart Interconnectome (BHI) combines neurology and cardiology but is hindered by inefficiencies in evidence synthesis, poor adherence to quality standards, and research waste. To address these challenges, we developed an AI-driven system to enhance systematic reviews in the BHI domain. The system integrates automated detection of Population, Intervention, Comparator, Outcome, and Study design (PICOS), semantic search using vector embeddings, graph-based querying, and topic modeling to identify redundancies and underexplored areas. Core components include a Bi-LSTM model achieving 87% accuracy for PICOS compliance, a study design classifier with 95.7% accuracy, and Retrieval-Augmented Generation (RAG) with GPT-3.5, which outperformed GPT-4 for graph-based and topic-driven queries. The system provides real-time updates, reducing research waste through a living database and offering an interactive interface with dashboards and conversational AI. While initially developed for BHI, the system's adaptable architecture enables its application across various biomedical fields, supporting rigorous evidence synthesis, efficient resource allocation, and informed clinical decision-making.