Nowcasting epidemic trends using hospital- and community-based virologic test data

Epidemiological surveillance typically relies on reported incidence of cases or hospitalizations, which can suffer significant reporting lags, biases and under-ascertainment. Here, we evaluated the potential of viral loads measured by RT-qPCR cycle threshold (Ct) values to track epidemic trends. We used SARS-CoV-2 RT-qPCR results from hospital testing in Massachusetts, USA, municipal testing in California, USA, and simulations to identify predictive models and covariates that maximize short-term epidemic trend prediction accuracy. We found SARS-CoV-2 Ct value distributions correlated with epidemic growth rates under real-world conditions. We fitted generalized additive models to predict log growth rate or direction of reported SARS-CoV-2 case incidence using features of the time-varying population Ct distribution and assessed the models’ ability to track epidemic dynamics in rolling two-week windows. Observed Ct value distributions accurately predicted epidemic growth rates (growth rate RMSE ∼ 0.039-0.052) and direction (AUC ∼ 0.72-0.78). Performance degraded during periods of rapidly changing growth rate. Predictive models were robust to testing regimes and sample sizes; accounting for population immunity or symptom status yielded no substantial improvement. Trimming Ct value outliers improved performance. These results indicate that analysis of Ct values from routine PCR tests can help monitor epidemic trends, complementing traditional incidence metrics.