Abstract
Many locations around the world have used real-time estimates of the time-varying effective reproductive number ({R}_{t}) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of {R}_{t} are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the latent period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of {R}_{t} based on case counts. We demonstrate that cycle threshold values could be used to improve real-time {R}_{t} estimation, enabling more timely tracking of epidemic dynamics.
Highlights
Many locations around the world have used real-time estimates of the time-varying effective reproductive number (Rt) of COVID-19 to provide evidence of transmission intensity to inform control strategies
We analyzed the first available record of cycle threshold (Ct) value for each confirmed case and characterized the daily distribution of Ct values that were sorted by sampling days
In this study, we applied a simplified Ct-based method to provide precise estimates of daily Rt and demonstrated that such a method could be used for real-time Rt estimation
Summary
Many locations around the world have used real-time estimates of the time-varying effective reproductive number (Rt) of COVID-19 to provide evidence of transmission intensity to inform control strategies. We analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of Rt based on case counts. A recent study showed that the distribution of viral loads among confirmed cases can provide inferences on transmission dynamics within populations, where population-level Ct values skewing towards lower values indicate more individuals have been recently infected, corresponding to an increasing rate of epidemic growth in the community, especially where single strain dominates[13]. We incorporated Ct values from COVID-19 cases in Hong Kong, a location with intense surveillance and case-finding efforts, to demonstrate that including data on population viral load distributions from symptom-based surveillance could support real-time tracking of transmission
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