Machine learning models predict pathogen risk in drinking water

Drinking water sources can contain diverse bacterial and viral pathogens, making reliable monitoring essential for public health protection. Conventional surveillance commonly relies on fecal indicator bacteria, but these organisms do not consistently reflect the presence of specific pathogens, especially viruses. Culture methods can take several days, while molecular testing requires specialized equipment and trained personnel. Machine learning offers a way to estimate microbial concentrations from routinely collected environmental measurements, capturing relationships that simple linear models may miss. However, much previous work has focused on indicator bacteria rather than pathogens themselves. Connecting pathogen predictions with health risk assessment therefore remains an important research need, alongside determining whether these models can provide useful, interpretable information across changing environmental conditions and different monitoring periods.

A study (DOI: 10.48130/biocontam-0026-0009) published in Biocontaminant on 10 July 2026 by Changzheng Cui's team, East China University of Science and Technology, shows that environmental measurements could support predictions of multiple microbial targets and generate potential health risk estimates broadly consistent with calculations based on measured pathogen concentrations.

The researchers collected 95 surface water samples from five fixed sites across two drinking water sources in an eastern Chinese city between May 2024 and December 2025. They measured seven water quality parameters, including temperature, turbidity, conductivity, dissolved oxygen, and total dissolved solids, and incorporated six meteorological variables. Membrane filtration quantified three indicator bacteria, while quantitative polymerase chain reaction measured six pathogens: Pseudomonas aeruginosa, Salmonella spp., Shigella spp., adenovirus, norovirus, and enterovirus. Six algorithms were compared, spanning linear regression, boosting, decision trees, support vector machines, random forests, and neural networks. Sampling batches were kept together when dividing training and test datasets, and grouped cross-validation supported model optimization.

Additional samples collected in January and February 2026 provided independent temporal validation. Microbial measurements revealed frequent contamination and weak or inconsistent relationships between bacterial indicators and viruses. Random forests and decision trees performed best for most targets. The selected models achieved reported coefficients of determination above 0.75, with the decision tree for P. aeruginosa exceeding 0.90. Temporal validation generally supported predictive performance, although uncertainty remained greater for several pathogens. SHapley Additive exPlanations analysis identified turbidity as a dominant contributor to indicator bacteria predictions, while pathogen models depended on differing combinations of environmental variables. The team then incorporated predicted concentrations into quantitative microbial risk assessment and used 10,000 Monte Carlo iterations to characterize uncertainty. Predicted disease burden distributions broadly matched those calculated from measured concentrations. Under the modeled exposure and treatment assumptions, Salmonella, Shigella, and enterovirus had estimated probabilities of 31.68%, 59.70%, and 20.86%, respectively, of exceeding the study's health burden benchmark. Disinfection efficiency was the strongest driver of risk variability. These estimates describe potential risks under specified scenarios; molecular detection measures genetic material and does not independently establish pathogen infectivity or actual illness among consumers. The findings also highlight treatment performance as central to controlling microbial exposure.

The study provides a practical route from routine environmental monitoring to interpretable estimates of microbial hazards and potential health consequences. Further validation across independent watersheds, seasons, and treatment settings is needed before wider deployment. Integration with real-time monitoring platforms could eventually support targeted testing, timely interventions, and more responsive management of drinking water sources.