Table 1.
Summary of WBE models for COVID-19 surveillance.
Fig 1.
Concept of applying machine learning for multi-community COVID-19 outbreak predictions with wastewater surveillance.
Fig 2.
a) Input type and sliding window; b) LSTM model architecture.
Fig 3.
Comparison of model performance.
a) RMSEs of five machine/deep learning models; b) Person’s correlation coefficient of the predicted (LSTM and Prophet model) vs. observed COVID-19 case numbers (15 days rolling average) for Athens (‘bad case’); c) LSTM model performance on the data from Athens sewershed in Ohio (Overlaid area plot of the predicted vs. observed COVID-19 case numbers. Potential undertesting was observed); and d) Prophet model on all sewersheds. The shaded area indicated the 95% credible interval of the model parameters. True observations are shown in solid dots.