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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 (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.
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.
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1 The University of Hong Kong, School of Public Health, LKS Faculty of Medicine, Hong Kong SAR, China (GRID:grid.194645.b) (ISNI:0000000121742757); The University of Hong Kong, HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, Hong Kong SAR, China (GRID:grid.194645.b) (ISNI:0000000121742757)
2 The University of Hong Kong, School of Public Health, LKS Faculty of Medicine, Hong Kong SAR, China (GRID:grid.194645.b) (ISNI:0000000121742757)
3 The Hong Kong Polytechnic University, Department of Health Technology and Informatics, Hong Kong SAR, China (GRID:grid.16890.36) (ISNI:0000 0004 1764 6123)
4 The University of Hong Kong, School of Public Health, LKS Faculty of Medicine, Hong Kong SAR, China (GRID:grid.194645.b) (ISNI:0000000121742757); Hong Kong Science and Technology Park, New Territories, Laboratory of Data Discovery for Health, Hong Kong SAR, China (GRID:grid.493736.c)
5 The University of Hong Kong, School of Public Health, LKS Faculty of Medicine, Hong Kong SAR, China (GRID:grid.194645.b) (ISNI:0000000121742757); Hong Kong Science and Technology Park, New Territories, Centre for Immunology & Infection, Hong Kong SAR, China (GRID:grid.194645.b)
6 The University of Hong Kong, School of Public Health, LKS Faculty of Medicine, Hong Kong SAR, China (GRID:grid.194645.b) (ISNI:0000000121742757); The University of Hong Kong, HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, Hong Kong SAR, China (GRID:grid.194645.b) (ISNI:0000000121742757); Hong Kong Science and Technology Park, New Territories, Centre for Immunology & Infection, Hong Kong SAR, China (GRID:grid.194645.b)