Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmissibility

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 incubation 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 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.