Predictive water virology using regularized regression analyses for projecting virus inactivation efficiency in ozone disinfection.

Abstract in brief

• The study develops models using machine learning algorithms to predict how effectively ozone disinfection in wastewater treatment removes viruses like norovirus, rotavirus, poliovirus, and coxsackievirus, considering water quality and operational factors.
• These models aim to improve the reliability of disinfection by estimating virus log reduction values (LRVs), helping maintain consistent pathogen inactivation despite variations in water quality.
• The approach can assist wastewater plant operators and risk assessors in setting critical control limits (CLs) to ensure public health safety, though further data collection is needed to enhance model accuracy and adaptability.]