Estimating the COVID-19 prevalence from wastewater

Area of Science:

• Environmental science
• Epidemiology
• Public health

Background:

• Wastewater-based epidemiology monitors pathogen trends like SARS-CoV-2.
• Viral load data alone has limitations for public health decisions.
• A cohort study (SentiSurv) was established to complement wastewater data.

Purpose of the Study:

• To calibrate wastewater viral load data with population prevalence data.
• To develop a method for estimating SARS-CoV-2 prevalence using wastewater data.
• To create a forecasting tool for future prevalence trends.

Main Methods:

• Utilized biweekly wastewater samples from 16 sites in Rhineland-Palatinate.
• Collected SARS-CoV-2 self-test data from up to 12,000 participants in the SentiSurv study.
• Applied an epidemiological model to calibrate wastewater viral load (normalized by PMMoV) with SentiSurv prevalence data.

Main Results:

• Identified a specific scaling factor and delay between normalized wastewater viral load and population prevalence.
• Demonstrated the utility of the calibrated model for prevalence estimation post-cohort data availability.
• Showcased the model's capability as a forecasting instrument for future trends.

Conclusions:

• Calibrated wastewater surveillance provides a reliable method for estimating and forecasting SARS-CoV-2 prevalence.
• The normalization method for wastewater samples significantly impacts calibration and forecasting accuracy.
• This integrated approach enhances public health surveillance capabilities.