Due to the variation in the SARS-CoV-2 virus, COVID-19 exhibits significant variability in severity. This presents challenges for governments in managing the allocation of healthcare resources and prioritizing health interventions. Clinical severity is also a critical statistical parameter for researchers to quantify the risks of infectious disease, model the transmission of COVID-19, and provide some targeted measures to control the pandemic. To obtain more accurate severity estimates, including confirmed case-hospitalization risk, confirmed case-fatality risk, hospitalization-fatality risk, and hospitalization-ICU risk, we conducted a systematic review and meta-analysis on the clinical severity (including hospitalization, ICU, and fatality risks) of different variants during the period of COVID-19 mass vaccination and provided pooled estimates for each clinical severity metric. All searches were carried out on 1 February 2022 in PubMed for articles published from 1 January 2020 to 1 February 2022. After identifying a total of 3536 studies and excluding 3523 irrelevant studies, 13 studies were included. The severity results show that the Delta and Omicron variants have the highest (6.56%, 0.46%, 19.63%, and 9.06%) and lowest severities (1.51%, 0.04%, 6.01%, and 3.18%), respectively, according to the four clinical severity metrics. Adults over 65 have higher severity levels for all four clinical severity metrics.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variants have a higher transmissibility and immune escape potential than previous variants, such as Delta variants, leading to their rapid spread around the world. In the mainland of China, >770 000 local cases have been reported from 1 January to 1 July 2022, compared with ~20 000 local cases in 2021. ¹ In Hong Kong, >1 200 000 local cases (greater than 15% of the population) have been reported in the same period. From early 2020, the mainland of China and Hong Kong have adopted hybrid non-pharmaceutical interventions (NPIs) (including school closures, workplace closures, public events cancellations and travel bans ²) to contain the recurring outbreaks. ³ Here, we conducted an exploratory analysis of potential correlates of COVID-19 real-time reproduction number (R_t) and specific NPIs across four cities in China with large outbreaks of Omicron variants. We investigated these correlates in relation to R_t to disentangle the factors that prevented the spread of Omicron, among the NPIs that policy makers can control.