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Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong.

Authors :
Xie, Ruopeng
Edwards, Kimberly M.
Adam, Dillon C.
Leung, Kathy S. M.
Tsang, Tim K.
Gurung, Shreya
Xiong, Weijia
Wei, Xiaoman
Ng, Daisy Y. M.
Liu, Gigi Y. Z.
Krishnan, Pavithra
Chang, Lydia D. J.
Cheng, Samuel M. S.
Gu, Haogao
Siu, Gilman K. H.
Wu, Joseph T.
Leung, Gabriel M.
Peiris, Malik
Cowling, Benjamin J.
Poon, Leo L. M.
Source :
Nature Communications; 4/27/2023, Vol. 14 Issue 1, p1-11, 11p
Publication Year :
2023

Abstract

Hong Kong experienced a surge of Omicron BA.2 infections in early 2022, resulting in one of the highest per-capita death rates of COVID-19. The outbreak occurred in a dense population with low immunity towards natural SARS-CoV-2 infection, high vaccine hesitancy in vulnerable populations, comprehensive disease surveillance and the capacity for stringent public health and social measures (PHSMs). By analyzing genome sequences and epidemiological data, we reconstructed the epidemic trajectory of BA.2 wave and found that the initial BA.2 community transmission emerged from cross-infection within hotel quarantine. The rapid implementation of PHSMs suppressed early epidemic growth but the effective reproduction number (R<subscript>e</subscript>) increased again during the Spring festival in early February and remained around 1 until early April. Independent estimates of point prevalence and incidence using phylodynamics also showed extensive superspreading at this time, which likely contributed to the rapid expansion of the epidemic. Discordant inferences based on genomic and epidemiological data underscore the need for research to improve near real-time epidemic growth estimates by combining multiple disparate data sources to better inform outbreak response policy. Hong Kong experienced a large wave of COVID-19 in early 2022 driven by Omicron BA.2. Here, the authors describe the epidemiological dynamics of this wave and show discordant inferences based on genomic and epidemiological data that underscore the need to improve near real-time epidemic growth estimates. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20411723
Volume :
14
Issue :
1
Database :
Complementary Index
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
163388199
Full Text :
https://doi.org/10.1038/s41467-023-38201-5