Effective real-time transmission estimations incorporating population viral load distributions amid SARS-CoV-2 variants and pre-existing immunity

Abstract

Background: Population-level viral load distribution, measured by cycle threshold (Ct), has been demonstrated to enable real-time estimation of 𝑅𝑡 for SARS-CoV-2 ancestral strain. Generalisability of the framework under different circulating variants and pre-existing immunity remains unclear. Aim: This study aimed to examine the impact of evolving variants and population immunity on the generalizability of Ct-based transmission estimation framework. Methods: We obtained the first Ct record of local COVID-19 cases from July 2020 to January 2023 in Hong Kong. We modeled the association between daily viral load distribution and the conventional estimates of 𝑅𝑡 based on case count. We trained the model using data from wave 3 (i.e., ancestral strain with minimal population immunity) and predicted 𝑅𝑡 for wave 5, 6 and 7 (i.e., omicron subvariants with > 70% vaccine coverage). Cross-validation was performed by training on the other 4 waves. Stratification analysis by disease severity was conducted to evaluate the impact of the changing severity profiles. Results: Trained with the ancestral dominated wave 3, our model provided accurate estimation of 𝑅𝑡, with the area under the ROC curve of 0.98 (95% confidence interval: 0.96, 1.00), 0.62 (95% CI: 0.53, 0.70) and 0.80 (95% CI: 0.73, 0.88) for three omicron dominated waves 5 to 7, respectively. Models trained on the other four waves also had high accuracy. Stratification analysis suggested potential impact of case severity on model estimation, which coincided with the fluctuation of sampling delay. Discussion: Our findings suggested that incorporating population viral shedding can provide accurate real-time estimation of transmission with evolving variants and population immunity. Application of the model needs to account for sampling delay.