An interpretable time series machine learning method for varying forecast and nowcast lengths in wastewater-based epidemiology.

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.

A time series based machine learning strategy for wastewater-based forecasting and nowcasting of COVID-19 dynamics.

Monitoring COVID-19 infection cases has been a singular focus of many policy makers and communities. However, direct monitoring through testing has become more onerous for a number of reasons, such as costs, delays, and personal choices. Wastewater-based epidemiology (WBE) has emerged as a viable tool for monitoring disease prevalence and dynamics to supplement direct monitoring. The objective of this study is to intelligently incorporate WBE information to nowcast and forecast new weekly COVID-19 cases and to assess the efficacy of such WBE information for these tasks in an interpretable manner. The methodology consists of a time-series based machine learning (TSML) strategy that can extract deeper knowledge and insights from temporal structured WBE data in the presence of other relevant temporal variables, such as minimum ambient temperature and water temperature, to boost the capability for predicting new weekly COVID-19 case numbers. The results confirm that feature engineering and machine learning can be utilized to enhance the performance and interpretability of WBE for COVID-19 monitoring, along with identifying the different recommended features to be applied for short-term and long-term nowcasting and short-term and long-term forecasting. The conclusion of this research is that the proposed time-series ML methodology performs as well, and sometimes better, than simple predictions that assume available and accurate COVID-19 case numbers from extensive monitoring and testing. Overall, this paper provides an insight into the prospects of machine learning based WBE to the researchers and decision-makers as well as public health practitioners for predicting and preparing the next wave of COVID-19 or the next pandemic.