Wastewater-based epidemiology has emerged as a viable tool for monitoring disease prevalence in a population. This paper details a time series machine learning (TSML) method for predicting COVID-19 cases from wastewater and environmental variables. The TSML method utilizes a number of techniques to create an interpretable, hypothesis-driven framework for machine learning that can handle different nowcast and forecast lengths. Some of the techniques employed include: • Feature engineering to construct interpretable features, like site-specific lead times, hypothesized to be potential predictors of COVID-19 cases. • Feature selection to identify features with the best predictive performance for the tasks of nowcasting and forecasting. • Prequential evaluation to prevent data leakage while evaluating the performance of the machine learning algorithm. Graphical abstract Image, graphical abstract
【저자키워드】 prediction, Feature selection, gradient boosting, Pandemic management, ARIMA model, Feature engineering, Stationarity, Temporal process, Time Series Machine Learning (TSML),