Effective Real-time Transmission Estimations Incorporating Population Viral Load Distributions Amid SARS-CoV-2 Variants and Preexisting Immunity

Abstract

Background. Population-level cycle threshold (Ct) distribution allows for Rt estimation for SARS-CoV-2 ancestral strain, however, its generalizability under different circulating variants and preexisting immunity remains unclear. Methods. We obtained the first Ct record of local COVID-19 cases from July 2020 to January 2023 in Hong Kong. The log-linear regression model, fitting on daily Ct mean and skewness to Rt estimated by case count, was trained with data from ancestral-dominated wave (minimal population immunity), and we predicted the Rt for Omicron waves (>70% vaccine coverage). Cross-validation was performed by training on other waves. Stratification analysis was conducted to retrospectively evaluate the impact of the changing severity profiles. Results. Model trained with the ancestral-dominated wave accurately estimated whether Rt was >1, with areas under the receiver operating characteristic curve of 0.98 (95% CI, 0.96–1.00), 0.62 (95% CI, 0.53–0.70), and 0.80 (95% CI, 0.73–0.88) for Omicron-dominated waves, respectively. Models trained on other waves also had discriminative performance. Stratification analysis suggested the potential impact of case severity on model estimation, which coincided with sampling delay. Conclusions. Incorporating population viral shedding can provide timely and accurate transmission estimation with evolving variants and population immunity, though model application should consider sampling delay.