Integrated Modeling for Predicting COVID-19 Outbreaks: Wastewater and Hospitalization Insights

Wastewater surveillance plays a pivotal role in early COVID-19 detection, fostering a proactive approach to public health. This study aims to establish a surveillance system utilizing wastewater data for forecasting COVID-19 cases before their occurrence and understanding their implications for healthcare facilities. It seeks to anticipate the hospitalization rates during outbreaks, enabling timely resource allocation and response. The system employs various machine learning algorithms to forecast COVID-19 surges based on the level of SARS-CoV-2 in the wastewater. The wastewater data is obtained from the Centers for Disease Control and Prevention's (CDC) National Wastewater Surveillance System. It also implements a predictive model for forecasting resulting hospitalization rates. Initial development using individual county data is rationalized by its representative nature, with plans for nationwide expansion. This model's development aims to enhance public awareness of outbreaks, facilitating transmission precautions and motivating facilities to prepare resources based on predicted patient volumes.