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.