Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection.

ABSTRACT: Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a gr

SUBMITTER: Rufino J 

PROVIDER: S-EPMC9844193 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature