Forecasting Urban Wastewater Microbiome Dynamics Using a Digital Twin Framework

Urban wastewater microbiomes are complex and temporally dynamic, offering valuable insight into community-scale microbialecology and potential public health trends. However, existing wastewater-based studies often remain descriptive, lacking toolsfor predictive modeling. In this study, we introduce a digital twin framework that forecasts microbial abundance trajectories inurban wastewater using an interpretable generative model, Q-net. Trained on a 30-week longitudinal metagenomic dataset fromseven wastewater treatment plants, the model captures temporal microbial dynamics with high fidelity (R² > 0.97 for key taxa;R² = 0.998 at the final timepoint). Beyond accurate forecasting, Q-net provides transparent model structure through conditionalinference trees and enables simulation of realistic microbial trends under hypothetical scenarios. This work demonstrates thepotential of digital twins to move wastewater microbiome studies from static snapshots to dynamic, predictive systems, withbroad implications for environmental monitoring and microbial ecosystem modeling