AI-Enhanced Wastewater Metagenomics: Tracking Pathogens for Community Health Surveillance

Renee Street of the South African Medical Research Council in South Africa and Samuel Scarpino of Northeastern University in the U.S. will perform a proof-of-concept study of AI for wastewater-based epidemiology by fine-tuning Large Language Models (LLMs) with SARS-CoV-2 metagenome sequence data to detect the emergence of viral variants. Monitoring wastewater is a useful surveillance tool that encompasses infected individuals with varying disease severity and access to healthcare facilities. They will fine-tune two LLMs to identify the emergence of viral variants using two existing SARS-CoV-2 metagenome sequence data sets: data processed with established bioinformatics to identify variants and data collected from wastewater before, during, and after the emergence of the Omicron variant. Success of the models will help validate an AI-based approach for monitoring microorganisms in wastewater to track the prevalence of specific health threats and forecast disease outbreaks, enabling targeted public health interventions.