tetano
Editor, Senior Moderator
BMC Public Health
. 2025 Aug 25;25(1):2910.
doi: 10.1186/s12889-025-24149-2. Wastewater as an early indicator for short-term forecasting COVID-19 hospitalization in Germany
Jonas Radermacher[SUP] 1 [/SUP], Steffen Thiel[SUP] 2 [/SUP], Aimo Kannt[SUP] 3 4 [/SUP], Holger Fröhlich[SUP] 5 6 [/SUP]
Affiliations
Background: The COVID-19 pandemic has profoundly affected daily life and posed significant challenges for politics, the economy, and the education system. To better prepare for such situations and implement effective measures, it is crucial to accurately assess, monitor, and forecast the progression of a pandemic. This study examines the potential of integrating wastewater surveillance data to enhance an autoregressive COVID-19 forecasting model for Germany and its federal states.
Methods: First, we explore the cross-correlations between SARS-CoV-2 viral RNA load measured in wastewater and COVID-19 hospitalization considering different time-lags. Further, the study compares the performance of different models, including Random Forest regressors, XGBoost regressors, ARIMA models, linear regression, and ridge regression models, both with and without the use of wastewater data as predictors. For decision tree-based models, we also analyze the performance of fully cross-modal models that rely solely on viral load measurements to predict COVID-19 hospitalization rates.
Results: Our retrospective analysis suggest that wastewater data can potentially serve as an early warning indicator of impending trends in hospitalization at a national level, as it shows a strong correlation with hospitalization figures of up to 86% and tends to lead them by up to 8 days. Despite this, including wastewater data in the prediction models did not statistical significantly enhance the accuracy of COVID-19 hospitalization forecasts. The ARIMA model without the inclusion of wastewater viral load data emerged as the best-performing model, achieving a Mean Absolute Percentage Error of 4.76% forecasting hospitalization 7 days ahead. However, wastewater viral load proved to be a valuable standalone predictor, offering an objective alternative to classical surveillance methods for monitoring pandemic trends.
Conclusion: This study reinforces the potential of wastewater surveillance as an early warning tool for COVID-19 hospitalizations in Germany. While strong correlations were observed, the integration of wastewater data into predictive models did not improve their performance. Nevertheless, wastewater viral load serves as a valuable indicator for monitoring pandemic trends, suggesting its utility in public health surveillance and resource allocation. Further research may help to clarify the real-time applicability of wastewater data and expand its use to other pathogens and data sources.
Keywords: COVID-19; Forecasting; Machine Learning; Pandemic; Surveillance; Wastewater.
. 2025 Aug 25;25(1):2910.
doi: 10.1186/s12889-025-24149-2. Wastewater as an early indicator for short-term forecasting COVID-19 hospitalization in Germany
Jonas Radermacher[SUP] 1 [/SUP], Steffen Thiel[SUP] 2 [/SUP], Aimo Kannt[SUP] 3 4 [/SUP], Holger Fröhlich[SUP] 5 6 [/SUP]
Affiliations
- PMID: 40855486
- PMCID: PMC12376350
- DOI: 10.1186/s12889-025-24149-2
Background: The COVID-19 pandemic has profoundly affected daily life and posed significant challenges for politics, the economy, and the education system. To better prepare for such situations and implement effective measures, it is crucial to accurately assess, monitor, and forecast the progression of a pandemic. This study examines the potential of integrating wastewater surveillance data to enhance an autoregressive COVID-19 forecasting model for Germany and its federal states.
Methods: First, we explore the cross-correlations between SARS-CoV-2 viral RNA load measured in wastewater and COVID-19 hospitalization considering different time-lags. Further, the study compares the performance of different models, including Random Forest regressors, XGBoost regressors, ARIMA models, linear regression, and ridge regression models, both with and without the use of wastewater data as predictors. For decision tree-based models, we also analyze the performance of fully cross-modal models that rely solely on viral load measurements to predict COVID-19 hospitalization rates.
Results: Our retrospective analysis suggest that wastewater data can potentially serve as an early warning indicator of impending trends in hospitalization at a national level, as it shows a strong correlation with hospitalization figures of up to 86% and tends to lead them by up to 8 days. Despite this, including wastewater data in the prediction models did not statistical significantly enhance the accuracy of COVID-19 hospitalization forecasts. The ARIMA model without the inclusion of wastewater viral load data emerged as the best-performing model, achieving a Mean Absolute Percentage Error of 4.76% forecasting hospitalization 7 days ahead. However, wastewater viral load proved to be a valuable standalone predictor, offering an objective alternative to classical surveillance methods for monitoring pandemic trends.
Conclusion: This study reinforces the potential of wastewater surveillance as an early warning tool for COVID-19 hospitalizations in Germany. While strong correlations were observed, the integration of wastewater data into predictive models did not improve their performance. Nevertheless, wastewater viral load serves as a valuable indicator for monitoring pandemic trends, suggesting its utility in public health surveillance and resource allocation. Further research may help to clarify the real-time applicability of wastewater data and expand its use to other pathogens and data sources.
Keywords: COVID-19; Forecasting; Machine Learning; Pandemic; Surveillance; Wastewater.