Predictive surveillance and diagnosis of COVID-19: An integrative machine learning and wastewater multi-omics approach

COVID-19 has had major global impacts, highlighting the importance of robust predictive surveillance and diagnostic systems to ensure effective public health responses. Traditional surveillance methods based on passive case counting and diagnostic testing of individual patients often suffer from delays and resource constraints, preventing timely responses. This study proposed an integrative framework integrating machine learning (ML)-derived predictive surveillance with wastewater-based diagnosis, aiming to predict temporal trends in Korea and identify disease-causing agents. The ML model utilized crowdsourced COVID-19-related keywords, climatic, and environmental data, optimized via model selection and feature selection. The integrated data-driven model predicted COVID-19 cases over three years more accurately than those using single source data (i.e., baseline model). The explainable AI technique (i.e., helping to inform how the model made those predictive decisions) identified six keywords (reducing phlegm, throat pain, long COVID-19, sore throat, COVID-19 self-kit, and COVID-19 kit) as robust predictors of disease trends. In a proof-of-concept experiment, wastewater-based genotyping of disease-causing agents and affected human communities in sewershed areas was conducted. Metatranscriptomics of municipal wastewater was conducted to identify COVID-19 viral variants, evolutionarily related to those clinically isolated strains, distinguishable from conventional diagnostic testing of individual patients. Wastewater-derived metagenomics was also performed to assess genomic variation in the affected human populations. The integrative framework proposed in this study offers a rapid, cost-effective approach for the surveillance and diagnosis of COVID-19 and other infectious diseases, thus strengthening or complementing existing health systems.