Generalizing population RT-qPCR cycle threshold values-informed estimation of epidemiological dynamics: Impact of surveillance practices and pathogen variability

Journal article

Population-level viral load distributions, measured by RT-qPCR or qPCR cycle threshold (Ct) values from surveillance testing, can be used to estimate the time-varying reproductive number (Rt) in real-time during COVID-19 outbreaks. However, it remains unclear whether this approach can be broadly applied to other pathogens, sources of virologic test data, or surveillance strategies beyond those specifically implemented during the COVID-19 pandemic in Hong Kong. We systematically evaluated the accuracy of Ct-based Rt estimates using simulated epidemics under different surveillance testing systems and pathogen viral kinetics. Using area under the ROC curve (AUC) to assess accuracy in detecting epidemic growth or decline, we found that case ascertainment rates minimally impacted estimation accuracy, except when detection was heavily biased towards severe patients (AUC: 0.64, 95% CIs: 0.59 - 0.71) or during prolonged waves with stable Rt near one (AUC: 0.54, 0.48 - 0.64), compared to stable detection patterns over time (AUC 0.76, 0.66 - 0.82). By comparing model accuracies across different viral shedding patterns and by parameterizing our model using data from six respiratory pathogens, we found that model performance largely depends on a monotonic viral shedding trajectory following case detection. A pathogen that lacks such shedding pattern – for example, those with a viral peak after onset – exhibited lower accuracy (AUC: 0.58, 0.49 - 0.65). Overall, our findings demonstrate that Ct-based Rt estimation methods are generally accurate across diverse surveillance conditions and pathogen shedding patterns, supporting their practical use as a supplementary tool for timely transmission monitoring while highlighting limitations that warrant further consideration.

Authors: Lin, Y; Hay, JA; Meng, Y; Cowling, BJ; Yang, B

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS Computational Biology
• Volume: 21
• Issue: 9
• Pages: e1013527
• Article number: e1013527
• Publication date: 2025-09-29
• Acceptance date: 2025-09-15
• DOI: 10.1371/journal.pcbi.1013527
• EISSN: 1553-7358
• ISSN: 1553734X and 1553-734X
• Language: English