Forecasting COVID-19 Cases, Hospital Admissions, and Deaths Based on Wastewater SARS-CoV-2 Surveillance Using Gaussian Copula Time Series Marginal Regression Model

Modeling efforts are needed to predict trends in COVID-19 cases and related health outcomes, aiding in the development of management strategies and adaptation measures. This study was conducted to assess whether the SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalizations, and deaths using copula-based time series modeling. SARS-CoV-2 RNA load in wastewater in Chesapeake, VA, was measured using the RT-qPCR method. A Gaussian copula time series (CTS) marginal regression model, incorporating an autoregressive moving average model and Gaussian copula function, was used as a forecasting model. Wastewater SARS-CoV-2 viral loads were correlated with COVID-19 cases. The forecasted model with both Poisson and negative binomial marginal distributions yielded trends in COVID-19 cases that closely paralleled the reported cases, with 90% of the forecasted COVID-19 cases falling within the 99% confidence interval of the reported data. However, the model did not effectively forecast the trends and the rising cases of hospital admissions and deaths. The forecasting model was validated for predicting clinical cases and trends with a non-normal distribution in a time series manner. Additionally, the model showed potential for using wastewater SARS-CoV-2 viral load as a predictor for forecasting COVID-19 cases.