Wastewater-based epidemiology: deriving a SARS-CoV-2 data validation method to assess data quality and to improve...

Area of Science:

• Environmental science
• Public health
• Data science

Background:

• Accurate SARS-CoV-2 data is vital for public health decision-making.
• Wastewater-based epidemiology offers insights into infection trends but faces challenges due to complex sample matrices and system variability.
• Existing methods struggle with data quality due to factors like precipitation and industrial discharge.

Purpose of the Study:

• To develop and validate an automated method for assessing and improving the quality of SARS-CoV-2 data from wastewater treatment plants (WWTPs).
• To identify potential extreme events and enhance the reliability of wastewater-based epidemiological data.
• To establish data quality categories for WWTPs and laboratories.

Main Methods:

• Categorized WWTP and laboratory data quality based on outliers in reproduction rates and SARS-CoV-2 time series inflection points.
• Scrutinized statistical outliers in quality control parameters (QCPs) including flow rate, electrical conductivity, and surrogate viruses (e.g., pepper mild mottle virus).
• Investigated outliers in analyzed gene segment ratios to detect potential laboratory errors and assessed the correlation between QCP outliers and SARS-CoV-2 concentration outliers.

Main Results:

• Flow rate and gene segment ratios were most effective in identifying outliers in SARS-CoV-2 concentration curves, with variations across WWTPs and laboratories.
• Excluding data points based on QCP plausibility checks significantly improved overall data quality.
• The developed data quality categories showed good agreement with visual assessments.

Conclusions:

• High data quality is essential for accurate trend recognition at local and national levels.
• The proposed automated method can enhance health-related monitoring by improving wastewater data reliability.
• The model is adaptable and can be optimized for individual WWTPs to accommodate diverse operational conditions.