Wastewater-based surveillance as a tool for monitoring and estimating COVID-19 incidence and trends: Insights from Germany, 2022-2024.

BACKGROUND

Wastewater-based surveillance complements case-based surveillance systems by capturing pathogen signals shed in stool and other bodily excretions, enabling population-level monitoring independent of clinical testing. Its utility during the COVID-19 pandemic has been widely explored, but its responsiveness and interpretability relative to case-based systems remain insufficiently understood.

METHODS

We analyzed German nationwide data on COVID-19 or SARS-CoV-2 from July 2022 to December 2024, using wastewater surveillance and four case-based surveillance systems. These comprise syndromic surveillance systems at the population as well as the primary care level, and mainly laboratory-confirmed notification data, all aimed at monitoring COVID-19 incidence in Germany. We assessed agreement between wastewater viral load and disease incidence using visual inspection, cross-correlation analysis, and an estimated prevalence dynamic informed by a fecal shedding model. We derived retrospective translation factors and compared week-to-week trend directions between systems. Finally, we tested the predictive power of wastewater data using classification models to anticipate current week incidence trends.

RESULTS

Wastewater SARS-CoV-2 viral load closely correlates with COVID-19 incidence trends from case-based systems, showing similar timing of peaks and troughs without notable time lags. Cross-correlation coefficients are highest with syndromic surveillance systems (up to 0.87) and lowest with notification data (0.43). Retrospective translation into incidence estimates works well on average, but week-to-week translation varies considerably. Wastewater-based models correctly predict the current week's trend, as indicated by at least three of the four case-based systems, with about 68 % probability.

CONCLUSION

Wastewater surveillance correlates well with COVID-19 incidence, but real-time translation to incidence lacks precision. Trend prediction for the current week may demonstrate improved accuracy and may be valuable when case reporting is limited or delayed.