Cross-sectional Ct distributions from qPCR tests can provide an early warning signal for the spread of COVID-19 in communities

BackgroundSARS-CoV-2 PCR testing data has been widely used for COVID-19 surveillance. Existing COVID-19 forecasting models mainly rely on case counts, even though the binary PCR results provide a limited picture of the pandemic trajectory. Most forecasting models have failed to accurately predict the COVID-19 waves before they occur. Recently a model utilizing cross-sectional population cycle threshold (Ct) values obtained from PCR tests (Ct-based model) was developed to overcome the limitations of using only binary PCR results. In this study, we aimed to improve on COVID-19 forecasting models using features derived from the Ct-based model, to detect epidemic waves earlier than case-based trajectories. MethodsPCR data was collected weekly at Northeastern University (NU) between August 2020 and January 2022. The NU campus epidemic trajectories were generated from the campus incidence rates. In addition, epidemic trajectories were generated for Suffolk County, where NU is located, based on publicly available case-counts. A novel forecasting approach was developed by enhancing a recent deep learning model with Ct-based features, along with the models default features. For this, cross-sectional Ct values from PCR data were used to generate Ct-based epidemic trajectories, including effective reproductive rate (Rt) and incidence. The improvement in forecasting performance was compared using absolute errors and residual squared errors with respect to actual observed cases at the 7-day and 14-day forecasting horizons. The model was also tested prospectively over the period January 2022 to April 2022. ResultsRt estimates from the Ct-based model preceded NU campus and Suffolk County cases by 12 and 14 days respectively, with a three-way synched Spearman correlation of 0.57. Enhancing the forecasting models with Ct-based information significantly decreased absolute error and residual squared error compared to the original model without Ct features (p-value <0.001 for both 7 and 14-days forecasting horizons). ConclusionCt-based epidemic trajectories can herald an earlier signal for impending epidemic waves in the community and forecast transmission peaks. Moreover, COVID-19 forecasting models can be enhanced using these Ct features to improve their forecasting accuracy. Policy implicationsWe make the case that public health agencies should publish Ct values along with the binary positive/negative PCR results. Early and accurate forecasting of epidemic waves can inform public health policies and countermeasures which can mitigate spread.