Wastewater as an Early Indicator for Short-Term Forecasting COVID-19 Hospitalization in Germany DOI Creative Commons
Jonas Botz, Steffen Thiel,

Amal Abderrahmani

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 25, 2024

Abstract Background The COVID-19 pandemic has profoundly affected daily life and posed significant challenges for politics, the economy, education system. To better prepare such situations implement effective measures, it is crucial to accurately assess, monitor, forecast progression of a pandemic. This study examines potential integrating wastewater surveillance data enhance an autoregressive forecasting model Germany its federal states. Methods We explore correlations between viral load measured in hospitalization. compares performance models, including Random Forest regressors, XGBoost ARIMA linear regression, ridge regression both with without use as predictors. For decision tree-based we also analyze fully cross-modal models that rely solely on measurements predict hospitalization rates. Results Our findings suggest can serve early warning indicator impending trends at national level, shows strong correlation figures tends lead them by six seven days. Despite this, prediction did not significantly accuracy forecasts. emerged best-performing model, achieving Mean Absolute Percentage Error 4.69%. However, proved be valuable standalone predictor, offering cost-effective objective alternative classical methods monitoring trends. Conclusion reinforces tool hospitalizations Germany. While were observed, integration into predictive improve their performance. Nevertheless, serves trends, suggesting utility public health resource allocation. Future research should broader applications other pathogens conjunction diverse sources.

Language: Английский

Wastewater as an Early Indicator for Short-Term Forecasting COVID-19 Hospitalization in Germany DOI Creative Commons
Jonas Botz, Steffen Thiel,

Amal Abderrahmani

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 25, 2024

Abstract Background The COVID-19 pandemic has profoundly affected daily life and posed significant challenges for politics, the economy, education system. To better prepare such situations implement effective measures, it is crucial to accurately assess, monitor, forecast progression of a pandemic. This study examines potential integrating wastewater surveillance data enhance an autoregressive forecasting model Germany its federal states. Methods We explore correlations between viral load measured in hospitalization. compares performance models, including Random Forest regressors, XGBoost ARIMA linear regression, ridge regression both with without use as predictors. For decision tree-based we also analyze fully cross-modal models that rely solely on measurements predict hospitalization rates. Results Our findings suggest can serve early warning indicator impending trends at national level, shows strong correlation figures tends lead them by six seven days. Despite this, prediction did not significantly accuracy forecasts. emerged best-performing model, achieving Mean Absolute Percentage Error 4.69%. However, proved be valuable standalone predictor, offering cost-effective objective alternative classical methods monitoring trends. Conclusion reinforces tool hospitalizations Germany. While were observed, integration into predictive improve their performance. Nevertheless, serves trends, suggesting utility public health resource allocation. Future research should broader applications other pathogens conjunction diverse sources.

Language: Английский

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