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

Tse Yang Lim (), Sanjat Kanjilal, Shira Doron, Jessica A. Penney, Meredith Haddix, Tae Hee Koo, Phoebe Danza, Rebecca Fisher, Yonatan H. Grad and James A. Hay ()
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Tse Yang Lim: Harvard T.H. Chan School of Public Health, Center for Communicable Disease Dynamics
Sanjat Kanjilal: Amsterdam University Medical Center, Department of Medical Microbiology
Shira Doron: Tufts Medical Center, Division of Geographic Medicine and Infectious Diseases
Jessica A. Penney: Tufts Medical Center, Division of Geographic Medicine and Infectious Diseases
Meredith Haddix: Los Angeles County Department of Public Health, Acute Communicable Disease Control Program
Tae Hee Koo: Los Angeles County Department of Public Health, Acute Communicable Disease Control Program
Phoebe Danza: Los Angeles County Department of Public Health, Acute Communicable Disease Control Program
Rebecca Fisher: Los Angeles County Department of Public Health, Acute Communicable Disease Control Program
Yonatan H. Grad: Harvard T.H. Chan School of Public Health, Center for Communicable Disease Dynamics
James A. Hay: Harvard T.H. Chan School of Public Health, Center for Communicable Disease Dynamics

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Population viral loads measured by reverse transcription quantitative polymerase chain reaction (RT-qPCR) cycle threshold (Ct) values are an alternative to case counts and hospitalizations for tracking epidemic trends, but their strengths, limitations, and statistical power under various real-world conditions have not been explored. Here, we used SARS-CoV-2 RT-qPCR results from hospital testing in Massachusetts, USA, municipal testing in California, USA, and a combination of theory and simulation analysis to quantify biological and logistical factors impacting Ct-based epidemic nowcasting accuracy. We found that changes to peak viral load, viral growth and clearance rates, and sampling approach and delays all affect the relationship between growth rates and Ct values. We fitted generalized additive models to predict the growth rate and direction of SARS-CoV-2 incidence using time-varying Ct value distributions and assessed nowcasting accuracy over two-week windows. The model predicted epidemic growth rates and direction well from ideal synthetic data (growth rate root-mean-squared error (RMSE) of 0.0192; epidemic direction area under the receiver operating characteristic curve (AUC) of 0.910) but showed modest accuracy with real-world data (RMSE of 0.039-0.052; AUC of 0.72-0.80). Predictions were robust to testing regimes and sample sizes, and trimming outliers improved performance. Our results elucidate the possibilities and limitations of Ct value-based epidemic surveillance, highlighting where they may complement traditional incidence metrics.

Date: 2025
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DOI: 10.1038/s41467-025-65237-6

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