ABSTRACT
As COVID-19 is posing a serious threat to global health, the emerging mutation in SARS-CoV-2 genomes, for example, N501Y substitution, is one of the major challenges against control of the pandemic. Characterizing the relationship between mutation activities and the risk of severe clinical outcomes is of public health importance for informing the healthcare decision-making process. Using a likelihood-based approach, we developed a statistical framework to reconstruct a time-varying and variant-specific case fatality ratio (CFR), and to estimate changes in CFR associated with a single mutation empirically. For illustration, the statistical framework is implemented to the COVID-19 surveillance data in the United Kingdom (UK). The reconstructed instantaneous CFR gradually increased from 1.0% in September to 2.2% in November 2020 and stabilized at this level thereafter, which monitors the mortality risk of COVID-19 on a real-time basis. We identified a link between the SARS-CoV-2 mutation activity at molecular scale and COVID-19 mortality risk at population scale, and found that the 501Y variants may slightly but not significantly increase 18% of fatality risk than the preceding 501N variants. We found no statistically significant evidence of change in COVID-19 mortality risk associated with 501Y variants, and highlighted the real-time estimating potentials of the modelling framework.
ABSTRACT
Background: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) since the end of 2019. As of February 15, there were 56 COVID-19 cases confirmed in Hong Kong since the first case with symptom onset on January 23, 2020. Methods: Based on the publicly available surveillance data in Hong Kong, we identified 21 transmission events as of February 15, 2020. An interval censored likelihood framework is adopted to fit three different distributions including Gamma, Weibull, and lognormal, that govern the serial interval (SI) of COVID-19. We selected the distribution according to the Akaike information criterion corrected for small sample size (AICc). Findings: We found the lognormal distribution performed slightly better than the other two distributions in terms of the AICc. Assuming a lognormal distribution model, we estimated the mean of SI at 4.9 days (95% CI: 3.6–6.2) and SD of SI at 4.4 days (95% CI: 2.9–8.3) by using the information of all 21 transmission events. Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak. © Copyright © 2020 Zhao, Gao, Zhuang, Chong, Cai, Ran, Cao, Wang, Lou, Wang, Yang, He and Wang.