Wastewater as an Early Indicator for Short-Term Forecasting COVID-19 Hospitalization in Germany

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 We explore the correlations between viral load measured in wastewater and COVID-19 hospitalization. The study compares the performance of autoregressive 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 findings suggest that wastewater data can serve as an early warning indicator of impending trends in hospitalization at a national level, as it shows a strong correlation with hospitalization figures and tends to lead them by six to seven days. Despite this, including wastewater data in the prediction models did not significantly enhance the accuracy of COVID-19 hospitalization forecasts. The ARIMA model emerged as the best-performing model, achieving a Mean Absolute Percentage Error of 4.69%. However, wastewater viral load proved to be a valuable standalone predictor, offering a cost-effective and 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. Future research should explore broader applications of wastewater data for other pathogens and in conjunction with diverse data sources.