A multivariate analysis to explain residue errors in pathogen concentration in wastewater-based epidemiology.

Area of Science:

• Environmental microbiology
• Epidemiology
• Wastewater analysis

Background:

• Wastewater-based epidemiology (WBE) surged during COVID-19 for population health monitoring.
• WBE predicts health cases by analyzing wastewater pathogen concentrations.
• Current WBE prediction models face errors due to dilution, decay, and wastewater matrix effects.

Purpose of the Study:

• To identify key wastewater-based epidemiologically relevant parameters (WBERPs) influencing prediction errors.
• To evaluate the impact of WBERPs on the accuracy of WBE case predictions.
• To compare model performance across different wastewater catchment types.

Main Methods:

• Utilized linear regression and random forest regression for case prediction.
• Employed random forest regression to determine WBERP importance.
• Studied two catchments: one with combined sewers, another with separate sewers.

Main Results:

• Variants of concern (VOCs) were identified as the most crucial time-variable WBERP.
• Including VOCs improved prediction accuracy by approximately 30% (RMSE) in both catchments.
• Physicochemical parameters and fecal markers showed only marginal improvements.
• Complex wastewater matrices (combined sewers) showed greater influence on WBE accuracy.

Conclusions:

• VOC data significantly enhances WBE predictive models but introduces practical challenges due to variant evolution.
• The dynamic nature of VOCs limits the quantitative accuracy of WBE predictions.
• Wastewater matrix complexity impacts the reliability of WBE, affecting the pathogen concentration-to-case relationship.