Forecasting Urban Wastewater Microbiome Dynamics Using a Digital Twin Framework - Dataset

Urban wastewater microbiomes are complex and temporally dynamic, offering valuable insight into community-scale microbial
ecology and potential public health trends. However, existing wastewater-based studies often remain descriptive, lacking tools
for predictive modeling. In this study, we introduce a digital twin framework that forecasts microbial abundance trajectories in
urban wastewater using an interpretable generative model, Q-net. Trained on a 30-week longitudinal metagenomic dataset from
seven 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 conditional
inference trees and enables simulation of realistic microbial trends under hypothetical scenarios. This work demonstrates the
potential of digital twins to move wastewater microbiome studies from static snapshots to dynamic, predictive systems, with
broad implications for environmental monitoring and microbial ecosystem modeling.
More detail in https://www.biorxiv.org/content/10.1101/2025.07.21.666059v1