Machine learning turns routine water quality data into early warnings for pathogen health risks

Safe drinking water depends not only on treatment, but also on knowing when harmful microorganisms may be present in source waters. Researchers have now developed a data-driven framework that uses routinely measured water quality indicators to predict pathogen concentrations and estimate their potential health risks.

The study, published in Biocontaminant, combines machine learning with quantitative microbial risk assessment, or QMRA, to create an ML-QMRA framework for drinking water source monitoring. The approach could complement conventional microbial testing by providing faster estimates of contamination risks from readily available water quality data.

“Routine monitoring already generates a large amount of environmental information. Our goal was to determine whether these commonly measured variables could also help us anticipate microbial contamination and translate those predictions into meaningful health risk estimates,” said Changzheng Cui, corresponding author of the study from East China University of Science and Technology.

Drinking water

The researchers collected 95 surface water samples from two drinking water sources in a major city in Eastern China between May 2024 and December 2025. They monitored three commonly used indicator bacteria, fecal coliforms, Escherichia coli, and Enterococcus faecalis, together with six pathogens: Pseudomonas aeruginosa, Salmonella spp., Shigella spp., adenovirus, norovirus, and enterovirus.

The results revealed an important limitation of conventional microbial indicators. Although the three fecal indicator bacteria were significantly correlated with one another, their relationships with viral pathogens were generally weak or inconsistent. This means bacterial indicators alone may not always reflect viral contamination accurately.

To improve prediction, the team compared six machine learning approaches, including Multiple Linear Regression, Least Squares Boosting, Decision Tree, Support Vector Machine, Random Forest, and Multilayer Perceptron models.

Random Forest and Decision Tree models performed particularly well, and all optimized models achieved R² values above 0.75. The Decision Tree model showed especially strong performance for P. aeruginosa, with an R² above 0.90. Independent data collected in January and February 2026 were also used for temporal validation, supporting the ability of most models to make predictions beyond the original training period.

Pathogen risks

The researchers then linked predicted pathogen concentrations to QMRA calculations expressed as disability-adjusted life years, or DALYs. Most estimated risks remained below the World Health Organization benchmark of 10⁻⁶ DALYs per person per year, but Salmonella spp., Shigella spp., and enterovirus showed probabilities of exceeding this benchmark under unfavorable exposure conditions.

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The analysis also identified disinfection efficiency as the dominant factor influencing estimated health risk, emphasizing the importance of stable and effective drinking water treatment.

To make the machine learning models more transparent, the researchers used SHapley Additive exPlanations, or SHAP. Turbidity was among the strongest predictors for fecal indicator bacteria, accounting for 41.6% to 62.1% of predictive importance in those models. Temperature, dissolved oxygen, rainfall, and other water quality variables contributed differently across individual pathogens.

Predictive information

The study shows that routine physicochemical measurements can provide useful predictive information about pathogen levels and their associated potential health risks. However, the authors stress that the framework still requires validation across different watersheds, seasons, treatment systems, and land-use conditions.

Future integration of ML-QMRA models with real-time monitoring systems could help water managers identify periods of elevated microbial risk earlier and support more targeted water safety interventions.