Clinical Severity of SARS-CoV-2 Variants during COVID-19 Vaccination: A Systematic Review and Meta-Analysis

Viruses. 2023 Sep 26;15(10):1994. doi: 10.3390/v15101994.

Abstract

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.

Keywords: COVID-19; SARS-CoV-2; meta-analysis; severity; systematic review; variants.

Publication types

  • Meta-Analysis
  • Systematic Review
  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • COVID-19 Vaccines
  • COVID-19* / epidemiology
  • COVID-19* / prevention & control
  • Humans
  • SARS-CoV-2* / genetics
  • Vaccination

Substances

  • COVID-19 Vaccines

Supplementary concepts

  • SARS-CoV-2 variants

Grants and funding

This work was supported by the Natural Science Foundation of Guangdong Province under grant 2020A1515010790, the National Natural Science Foundation of China under grant 62173236, the Guangdong Science and Technology Strategic Innovation Fund (Guangdong–Hong Kong-Macau Joint Laboratory Program) under grant 2020B1212030009, and in part by the Technology Research Project of Shenzhen City under grant JCYJ20190808174801673.