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http://dx.doi.org/10.1126/science.adm7123 | DOI Listing |
Science
December 2023
School of Ecology and Environment, Hainan University, Hainan 570228, China.
Med
November 2023
ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK.
PeerJ
September 2023
Department of Marine Biology, Pukyong National University, Busan, South Korea.
Int J Epidemiol
April 2023
International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) Global Support Centre, Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Elife
October 2022
ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom.
Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings.
Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries.
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