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

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作者
Ruopeng Xie
Kimberly M. Edwards
Dillon C. Adam
Kathy S. M. Leung
Tim K. Tsang
Shreya Gurung
Weijia Xiong
Xiaoman Wei
Daisy Y. M. Ng
Gigi Y. Z. Liu
Pavithra Krishnan
Lydia D. J. Chang
Samuel M. S. Cheng
Haogao Gu
Gilman K. H. Siu
Joseph T. Wu
Gabriel M. Leung
Malik Peiris
Benjamin J. Cowling
Leo L. M. Poon
Vijaykrishna Dhanasekaran
机构
[1] The University of Hong Kong,School of Public Health, LKS Faculty of Medicine
[2] The University of Hong Kong,HKU
[3] The Hong Kong Polytechnic University,Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine
[4] Hong Kong Science and Technology Park,Department of Health Technology and Informatics
[5] New Territories,Laboratory of Data Discovery for Health
[6] Hong Kong Science and Technology Park,Centre for Immunology & Infection
[7] New Territories,undefined
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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 (Re) 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.
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