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城市污水微生物组（urban wastewater microbiomes）结构复杂且具有时序动态性，可为群落尺度微生物生态学研究与潜在公共卫生趋势研判提供极具价值的视角。然而，现有基于污水的相关研究多停留在描述性阶段，缺乏可用于预测建模的工具。本研究提出了一种数字孪生（digital twin）框架，该框架依托可解释生成模型Q-net对城市污水中的微生物丰度动态轨迹进行预测。该模型基于来自7座污水处理厂的30周纵向宏基因组（metagenomic）数据集训练而成，能够高精度捕捉时序微生物动态特征：关键分类群（taxa）的决定系数R²>0.97，最终时间点的R²可达0.998。除精准预测外，Q-net还可通过条件推理树（conditional inference trees）展现透明的模型结构，并支持在假想场景下模拟真实可信的微生物动态趋势。本研究证实了数字孪生技术可推动污水微生物组研究从静态快照式研究转向动态可预测系统，对环境监测与微生物生态系统建模均具有广泛的应用价值。更多详细内容可参见：https://www.biorxiv.org/content/10.1101/2025.07.21.666059v1