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Li M, Grépin KA, Zhang R, Cowling BJ, Yang B. Assessing the effectiveness of travel control measures in preventing imported COVID-19 cases reveals the critical role of travel volume. Epidemics 2025; 51:100837. [PMID: 40398087 DOI: 10.1016/j.epidem.2025.100837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2024] [Revised: 03/26/2025] [Accepted: 05/07/2025] [Indexed: 05/23/2025] Open
Abstract
BACKGROUND Although travel control measures have played a key role in mitigating COVID-19 spread in certain regions, few empirical observational studies have specifically quantified their effectiveness in preventing the importation of infectious cases into communities. In Hong Kong, layered policies (e.g., mandatory quarantine, staggered testing protocols, and phased travel volume restriction) provided a natural experiment to disentangle these components. Our study evaluates the contributions of each measure to preventing imported infectious cases releasing to community. METHODS We retrospectively assessed these measures' effectiveness in Hong Kong, utilizing data from eight countries during 2020-2021. Data on imported COVID-19 cases, including departure origins and time from arrival to report, was compiled. To estimate the SARS-CoV-2 prevalence among inbound travelers, we used a Bayesian framework that accounted for the disease history and testing sensitivity and fitted to cases detected on arrival and travel volumes. We compared the number of prevented infections under the implemented measures to a scenario where no measures were taken. We also conducted counterfactual analysis to examine the independent and marginal effects of individual measures. RESULTS Stringent travel measures prevented 9821 (9065 - 10,564) importations from entering Hong Kong. Travel volume reductions had the greatest impact (93.0 % reduction, 95 % confidence interval, CI: 91.9 %-93.9 %), followed by mandatory quarantine (80.8 % reduction, 95 % CI: 75.7 % - 87.1 %). In-quarantine COVID-19 testing showed no substantial additional effectiveness in preventing infectious COVID-19 cases into community (81.8 % reduction, 95 % CI:74.8 %-87.1 %) beyond mandatory quarantine alone. CONCLUSIONS Our findings demonstrate that while stringent post-arrival measures effectively reduced community transmission of imported COVID-19 cases, travel volume reduction played a critical and independent role in limiting viral importation, regardless of post-arrival interventions.
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Affiliation(s)
- Mingwei Li
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Karen A Grépin
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ru Zhang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
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Lau SSS, Fong JWL, Cheng MCH. Psychological cost of Hong Kong's zero-COVID policy: three-wave repeated cross-sectional study of pandemic fatigue, pandemic fear and emotional well-being from peak pandemic to living-with-COVID policy shift. BJPsych Open 2025; 11:e68. [PMID: 40123452 PMCID: PMC12001963 DOI: 10.1192/bjo.2025.18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 12/19/2024] [Accepted: 01/19/2025] [Indexed: 03/25/2025] Open
Abstract
BACKGROUND Hong Kong's 3-year dynamic zero-COVID policy has caused prolonged exposure to stringent, pervasive anti-epidemic measures, which poses additional stressors on emotional well-being through pandemic fatigue, beyond the incumbent fear of the pandemic. AIMS To investigate how major policy shifts in the zero-COVID strategy have corresponded with changing relationships between emotional well-being, pandemic fatigue from policy adherence, and pandemic fear, following the pandemic peak to a living-with-COVID policy. METHOD A three-wave repeated cross-sectional study (N = 2266) was conducted on the Chinese working-age population (18-64 years) during the peak outbreak (Wave 1), and subsequent policy shifts towards a living-with-COVID policy during the initial relaxation (Wave 2) and full relaxation (Wave 3) of anti-epidemic measures from March 2022 to March 2023. Non-parametric tests, consisting of robust analysis of covariance tests and quantile regression analysis, were performed. RESULTS The severity of all measures was lowered after Wave 1; however, extreme pandemic fears reported in Wave 2 (n = 38, 7.7%) were associated with worse emotional well-being than the pandemic peak (Wave 1), which then subsided in Wave 3. Pandemic fatigue posed greater negative emotional well-being in Wave 1, whereas pandemic fear was the dominant predictor in Waves 2 and 3. CONCLUSIONS Pandemic fatigue and pandemic fear together robustly highlight the psychological cost of prolonged pandemic responses, expanding on a framework for monitoring and minimising the unintended mental health ramifications of anti-epidemic policies.
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Affiliation(s)
- Sam S. S. Lau
- Research Centre for Environment and Human Health, School of Continuing Education, Hong Kong Baptist University, Hong Kong, China
- College of International Education, School of Continuing Education, Hong Kong Baptist University, Hong Kong, China
| | - Jason W. L. Fong
- Research Centre for Environment and Human Health, School of Continuing Education, Hong Kong Baptist University, Hong Kong, China
| | - Marco C. H. Cheng
- Research Centre for Environment and Human Health, School of Continuing Education, Hong Kong Baptist University, Hong Kong, China
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3
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Martín-Sánchez M, Wu P, Adam DC, Yang B, Lim WW, Lin Y, Lau EH, Sullivan SG, Leung GM, Cowling BJ. An observational study on imported COVID-19 cases in Hong Kong during mandatory on-arrival hotel quarantine. PUBLIC HEALTH IN PRACTICE 2024; 8:100525. [PMID: 39050010 PMCID: PMC11267049 DOI: 10.1016/j.puhip.2024.100525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 05/23/2024] [Accepted: 06/05/2024] [Indexed: 07/27/2024] Open
Abstract
Background Hong Kong enforced stringent travel restrictions during the COVID-19 pandemic. Understanding the characteristics of imported COVID-19 cases is important for establishing evidence-based control measures. Methods Retrospective cohort study summarising the characteristics of imported cases detected in Hong Kong between 13 November 2020 and 31 January 2022, when compulsory quarantine was implemented. Findings A total of 2269 imported COVID-19 cases aged 0-85 years were identified, of which 48.6 % detected on arrival. A shorter median delay from arrival to isolation was observed in Delta and Omicron cases (3 days) than in ancestral strain and other variants cases (12 days; p < 0.001). Lower Ct values at isolation were observed in Omicron cases than in ancestral strain or other variants cases. No Omicron cases were detected beyond 14 days after arrival. Cases detected after 14 days of quarantine (n=58, 2.6 %) were more likely asymptomatic at isolation and had higher Ct value during isolation, some of them indicating re-positivity or post-arrival infections. Conclusions Testing inbound travellers at arrival and during quarantine can detect imported cases early, but may not prevent all COVID-19 introductions into the community. Public health measures should be adapted in response to the emergence of SARS-CoV-2 variants based on evidence from ongoing surveillance.
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Affiliation(s)
- Mario Martín-Sánchez
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Dillon C. Adam
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Wey Wen Lim
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yun Lin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H.Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Sheena G. Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Department of Epidemiology, University of California, Los Angeles, USA
| | - Gabriel M. Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
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Carnegie L, McCrone JT, du Plessis L, Hasan M, Ali MZ, Begum R, Hassan MZ, Islam S, Rahman MH, Uddin ASM, Sarker MS, Das T, Hossain M, Khan M, Razu MH, Akram A, Arina S, Hoque E, Molla MMA, Nafisaa T, Angra P, Rambaut A, Pullan ST, Osman KL, Hoque MA, Biswas P, Flora MS, Raghwani J, Fournié G, Samad MA, Hill SC. Genomic epidemiology of early SARS-CoV-2 transmission dynamics in Bangladesh. Virol J 2024; 21:291. [PMID: 39538264 PMCID: PMC11562509 DOI: 10.1186/s12985-024-02560-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 10/26/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Genomic epidemiology has helped reconstruct the global and regional movement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there is still a lack of understanding of SARS-CoV-2 spread in some of the world's least developed countries (LDCs). METHODS To begin to address this disparity, we studied the transmission dynamics of the virus in Bangladesh during the country's first COVID-19 wave by analysing case reports and whole-genome sequences from all eight divisions of the country. RESULTS We detected > 50 virus introductions to the country during the period, including during a period of national lockdown. Additionally, through discrete phylogeographic analyses, we identified that geographical distance and population -density and/or -size influenced virus spatial dispersal in Bangladesh. CONCLUSIONS Overall, this study expands our knowledge of SARS-CoV-2 genomic epidemiology in Bangladesh, shedding light on crucial transmission characteristics within the country, while also acknowledging resemblances and differences to patterns observed in other nations.
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Affiliation(s)
- L Carnegie
- Department of Pathobiology and Population Sciences, Royal Veterinary College (RVC), Hatfield, Hertfordshire, UK.
| | - J T McCrone
- Institute of Ecology and Evolution, University of Edinburgh, King's Buildings, Edinburgh, UK
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - L du Plessis
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - M Hasan
- Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka, Bangladesh
| | - M Z Ali
- Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka, Bangladesh
| | - R Begum
- Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka, Bangladesh
| | - M Z Hassan
- Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka, Bangladesh
| | - S Islam
- Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka, Bangladesh
- Global Change Center, Virginia Tech, Blacksburg, VA, USA
| | - M H Rahman
- Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka, Bangladesh
| | - A S M Uddin
- Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka, Bangladesh
| | - M S Sarker
- Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka, Bangladesh
| | - T Das
- Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, Bangladesh
- School of Agricultural, Environmental and Veterinary Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia
| | - M Hossain
- NSU Genome Research Institute (NGRI), North South University, Bashundhara, Dhaka, Bangladesh
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, Bangladesh
| | - M Khan
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhanmondi, Dhaka, Bangladesh
| | - M H Razu
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhanmondi, Dhaka, Bangladesh
| | - A Akram
- National Institute of Laboratory Medicine and Referral Centre (NILMRC), Agargoan, Dhaka, Bangladesh
| | - S Arina
- National Institute of Laboratory Medicine and Referral Centre (NILMRC), Agargoan, Dhaka, Bangladesh
| | - E Hoque
- National Institute of Laboratory Medicine and Referral Centre (NILMRC), Agargoan, Dhaka, Bangladesh
| | - M M A Molla
- National Institute of Laboratory Medicine and Referral Centre (NILMRC), Agargoan, Dhaka, Bangladesh
| | - T Nafisaa
- National Institute of Laboratory Medicine and Referral Centre (NILMRC), Agargoan, Dhaka, Bangladesh
| | - P Angra
- Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - A Rambaut
- Institute of Ecology and Evolution, University of Edinburgh, King's Buildings, Edinburgh, UK
| | - S T Pullan
- United Kingdom Health Security Agency (UKHSA), Porton Down, Salisbury, UK
| | - K L Osman
- United Kingdom Health Security Agency (UKHSA), Porton Down, Salisbury, UK
| | - M A Hoque
- Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, Bangladesh
| | - P Biswas
- Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, Bangladesh
| | - M S Flora
- National Institute of Preventive and Social Medicine (NIPSOM), Ministry of Health and Family Welfare, Dhaka, Bangladesh
| | - J Raghwani
- Department of Pathobiology and Population Sciences, Royal Veterinary College (RVC), Hatfield, Hertfordshire, UK
| | - G Fournié
- Department of Pathobiology and Population Sciences, Royal Veterinary College (RVC), Hatfield, Hertfordshire, UK
- Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, Marcy l'Etoile, France
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, Saint Genes Champanelle, France
| | - M A Samad
- Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka, Bangladesh.
| | - S C Hill
- Department of Pathobiology and Population Sciences, Royal Veterinary College (RVC), Hatfield, Hertfordshire, UK.
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5
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Xie R, Adam DC, Hu S, Cowling BJ, Gascuel O, Zhukova A, Dhanasekaran V. Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach. Mol Biol Evol 2024; 41:msae232. [PMID: 39497507 PMCID: PMC11600589 DOI: 10.1093/molbev/msae232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 09/27/2024] [Accepted: 10/24/2024] [Indexed: 11/28/2024] Open
Abstract
Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancements, including the application of deep learning to overcome computational limitations, significant challenges persist due to data inadequacies and statistical unidentifiability of key parameters. These issues are particularly pronounced in poorly resolved phylogenies, commonly observed in outbreaks such as SARS-CoV-2. In this study, we conducted a thorough evaluation of PhyloDeep, a deep learning inference tool for phylodynamics, assessing its performance on poorly resolved phylogenies. Our findings reveal the limited predictive accuracy of PhyloDeep (and other state-of-the-art approaches) in these scenarios. However, models trained on poorly resolved, realistically simulated trees demonstrate improved predictive power, despite not being infallible, especially in scenarios with superspreading dynamics, whose parameters are challenging to capture accurately. Notably, we observe markedly improved performance through the integration of minimal contact tracing data, which refines poorly resolved trees. Applying this approach to a sample of SARS-CoV-2 sequences partially matched to contact tracing from Hong Kong yields informative estimates of superspreading potential, extending beyond the scope of contact tracing data alone. Our findings demonstrate the potential for enhancing phylodynamic analysis through complementary data integration, ultimately increasing the precision of epidemiological predictions crucial for public health decision-making and outbreak control.
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Affiliation(s)
- Ruopeng Xie
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China
| | - Dillon C Adam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China
| | - Shu Hu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China
| | - Benjamin J Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong S.A.R., China
| | - Olivier Gascuel
- Biologie intégrative des populations, Evolution moléculaire (BIPEM), Institut de Systématique, Evolution, Biodiversité (ISYEB, UMR 7205—CNRS, MNHN, SU, EPHE, UA), Muséum National d’Histoire Naturelle, Paris 75005 France
| | - Anna Zhukova
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris 75015, France
- G5 Evolutionary Dynamics of Infectious Diseases, Institut Pasteur, Université de Paris, Paris 75015, France
| | - Vijaykrishna Dhanasekaran
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China
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6
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Hadi R, Poddar A, Sonnaila S, Bhavaraju VSM, Agrawal S. Advancing CRISPR-Based Solutions for COVID-19 Diagnosis and Therapeutics. Cells 2024; 13:1794. [PMID: 39513901 PMCID: PMC11545109 DOI: 10.3390/cells13211794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Revised: 10/19/2024] [Accepted: 10/25/2024] [Indexed: 11/16/2024] Open
Abstract
Since the onset of the COVID-19 pandemic, a variety of diagnostic approaches, including RT-qPCR, RAPID, and LFA, have been adopted, with RT-qPCR emerging as the gold standard. However, a significant challenge in COVID-19 diagnostics is the wide range of symptoms presented by patients, necessitating early and accurate diagnosis for effective management. Although RT-qPCR is a precise molecular technique, it is not immune to false-negative results. In contrast, CRISPR-based detection methods for SARS-CoV-2 offer several advantages: they are cost-effective, time-efficient, highly sensitive, and specific, and they do not require sophisticated instruments. These methods also show promise for scalability, enabling diagnostic tests. CRISPR technology can be customized to target any genomic region of interest, making it a versatile tool with applications beyond diagnostics, including therapeutic development. The CRISPR/Cas systems provide precise gene targeting with immense potential for creating next-generation diagnostics and therapeutics. One of the key advantages of CRISPR/Cas-based therapeutics is the ability to perform multiplexing, where different sgRNAs or crRNAs can target multiple sites within the same gene, reducing the likelihood of viral escape mutants. Among the various CRISPR systems, CRISPR/Cas13 and CARVER (Cas13-assisted restriction of viral expression and readout) are particularly promising. These systems can target a broad range of single-stranded RNA viruses, making them suitable for the diagnosis and treatment of various viral diseases, including SARS-CoV-2. However, the efficacy and safety of CRISPR-based therapeutics must be thoroughly evaluated in pre-clinical and clinical settings. While CRISPR biotechnologies have not yet been fully harnessed to control the current COVID-19 pandemic, there is an optimism that the limitations of the CRISPR/Cas system can be overcome soon. This review discusses how CRISPR-based strategies can revolutionize disease diagnosis and therapeutic development, better preparing us for future viral threats.
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Affiliation(s)
- Roaa Hadi
- Cell and Molecular Biology Program, Fulbright College of Arts and Sciences, University of Arkansas, Fayetteville, AR 72701, USA;
- Department of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Abhishek Poddar
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA;
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Shivakumar Sonnaila
- Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA;
| | | | - Shilpi Agrawal
- Department of Biomedical Engineering, College of Engineering, University of Arkansas, Fayetteville, AR 72701, USA
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7
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Zhao N, He M, Wang H, Zhu L, Wang N, Yong W, Fan H, Ding S, Ma T, Zhang Z, Dong X, Wang Z, Dong X, Min X, Zhang H, Ding J. Genomic epidemiology reveals the variation and transmission properties of SARS-CoV-2 in a single-source community outbreak. Virus Evol 2024; 10:veae085. [PMID: 39493536 PMCID: PMC11529616 DOI: 10.1093/ve/veae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 09/04/2024] [Accepted: 10/10/2024] [Indexed: 11/05/2024] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the coronavirus disease 2019 (COVID-19) pandemic, which is still a global public health concern. During March 2022, a rapid and confined single-source outbreak of SARS-CoV-2 was identified in a community in Nanjing municipal city. Overall, 95 individuals had laboratory-confirmed SARS-CoV-2 infection. The whole genomes of 61 viral samples were obtained, which were all members of the BA.2.2 lineage and clearly demonstrated the presence of one large clade, and all the infections could be traced back to the original index case. The most distant sequence from the index case presented a difference of 4 SNPs, and 118 intrahost single-nucleotide variants (iSNVs) at 74 genomic sites were identified. Some minor iSNVs can be transmitted and subsequently rapidly fixed in the viral population. The minor iSNVs transmission resulted in at least two nucleotide substitutions among all seven SNPs identified in the outbreak, generating genetically diverse populations. We estimated the overall transmission bottleneck size to be 3 using 11 convincing donor-recipient transmission pairs. Our study provides new insights into genomic epidemiology and viral transmission, revealing how iSNVs become fixed in local clusters, followed by viral transmission across the community, which contributes to population diversity.
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Affiliation(s)
- Ning Zhao
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - Min He
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
- School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China
| | - HengXue Wang
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - LiGuo Zhu
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Road, Nanjing, Jiangsu 210009, China
| | - Nan Wang
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - Wei Yong
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - HuaFeng Fan
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - SongNing Ding
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - Tao Ma
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - Zhong Zhang
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - XiaoXiao Dong
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - ZiYu Wang
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - XiaoQing Dong
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - XiaoYu Min
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - HongBo Zhang
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
| | - Jie Ding
- Microbiology Laboratory, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, 2 Zizhulin Road, Nanjing, Jiangsu 210003, China
- School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China
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8
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Yang B, Lin Y, Xiong W, Liu C, Gao H, Ho F, Zhou J, Zhang R, Wong JY, Cheung JK, Lau EH, Tsang TK, Xiao J, Wong IO, Martín-Sánchez M, Leung GM, Cowling BJ, Wu P. Comparison of control and transmission of COVID-19 across epidemic waves in Hong Kong: an observational study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 43:100969. [PMID: 38076326 PMCID: PMC10700518 DOI: 10.1016/j.lanwpc.2023.100969] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/03/2023] [Accepted: 11/01/2023] [Indexed: 08/04/2024]
Abstract
BACKGROUND Hong Kong contained COVID-19 for two years but experienced a large epidemic of Omicron BA.2 in early 2022 and endemic transmission of Omicron subvariants thereafter. We reflected on pandemic preparedness and responses by assessing COVID-19 transmission and associated disease burden in the context of implementation of various public health and social measures (PHSMs). METHODS We examined the use and impact of pandemic controls in Hong Kong by analysing data on more than 1.7 million confirmed COVID-19 cases and characterizing the temporal changes non-pharmaceutical and pharmaceutical interventions implemented from January 2020 through to 30 December 2022. We estimated the daily effective reproductive number (Rt) to track changes in transmissibility and effectiveness of community-based measures against infection over time. We examined the temporal changes of pharmaceutical interventions, mortality rate and case-fatality risks (CFRs), particularly among older adults. FINDINGS Hong Kong experienced four local epidemic waves predominated by the ancestral strain in 2020 and early 2021 and prevented multiple SARS-CoV-2 variants from spreading in the community before 2022. Strict travel-related, case-based, and community-based measures were increasingly tightened in Hong Kong over the first two years of the pandemic. However, even very stringent measures were unable to contain the spread of Omicron BA.2 in Hong Kong. Despite high overall vaccination uptake (>70% with at least two doses), high mortality was observed during the Omicron BA.2 wave due to lower vaccine coverage (42%) among adults ≥65 years of age. Increases in antiviral usage and vaccination uptake over time through 2022 was associated with decreased case fatality risks. INTERPRETATION Integrated strict measures were able to reduce importation risks and interrupt local transmission to contain COVID-19 transmission and disease burden while awaiting vaccine development and rollout. Increasing coverage of pharmaceutical interventions among high-risk groups reduced infection-related mortality and mitigated the adverse health impact of the pandemic. FUNDING Health and Medical Research Fund.
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Affiliation(s)
- Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yun Lin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Weijia Xiong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Chang Liu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Huizhi Gao
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Faith Ho
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jiayi Zhou
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ru Zhang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jessica Y. Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Justin K. Cheung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H.Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Tim K. Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jingyi Xiao
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Irene O.L. Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Mario Martín-Sánchez
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Gabriel M. Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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9
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Young BR, Yang B, Wu P, Adam DC, Wong JY, Ho F, Gao H, Lau EHY, Leung GM, Cowling BJ. Residential Clustering of Coronavirus Disease 2019 Cases and Efficiency of Building-Wide Compulsory Testing Notices as a Transmission Control Measure in Hong Kong. J Infect Dis 2023; 228:426-430. [PMID: 37094371 DOI: 10.1093/infdis/jiad107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 04/06/2023] [Accepted: 04/14/2023] [Indexed: 04/26/2023] Open
Abstract
We described the frequency of residential case clusters and the efficiency of compulsory testing in identifying cases using buildings targeted in compulsory testing and locally infected coronavirus disease 2019 (COVID-19) cases matched by residence in Hong Kong. Most of the buildings (4246 of 7688, 55.2%) with COVID-19 cases identified had only 1 reported case, and 13% of the daily reported cases were detected through compulsory testing. Compulsory testing notices could be essential in attempting to eliminate infections ("zero COVID") and have an impact early in an epidemic, but they appear to be relatively inefficient in response to sustained community transmission.
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Affiliation(s)
- Benjamin R Young
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Bingyi Yang
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peng Wu
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Dillon C Adam
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jessica Y Wong
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Faith Ho
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Huizhi Gao
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Gabriel M Leung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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10
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Puenpa J, Sawaswong V, Nimsamer P, Payungporn S, Rattanakomol P, Saengdao N, Chansaenroj J, Yorsaeng R, Suwannakarn K, Poovorawan Y. Investigation of the Molecular Epidemiology and Evolution of Circulating Severe Acute Respiratory Syndrome Coronavirus 2 in Thailand from 2020 to 2022 via Next-Generation Sequencing. Viruses 2023; 15:1394. [PMID: 37376693 PMCID: PMC10303178 DOI: 10.3390/v15061394] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 06/16/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is an infectious condition caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which surfaced in Thailand in early 2020. The current study investigated the SARS-CoV-2 lineages circulating in Thailand and their evolutionary history. Complete genome sequencing of 210 SARS-CoV-2 samples collected from collaborating hospitals and the Institute of Urban Disease Control and Prevention over two years, from December 2020 to July 2022, was performed using next-generation sequencing technology. Multiple lineage introductions were observed before the emergence of the B.1.1.529 omicron variant, including B.1.36.16, B.1.351, B.1.1, B.1.1.7, B.1.524, AY.30, and B.1.617.2. The B.1.1.529 omicron variant was subsequently detected between January 2022 and June 2022. The evolutionary rate for the spike gene of SARS-CoV-2 was estimated to be between 0.87 and 1.71 × 10-3 substitutions per site per year. There was a substantial prevalence of the predominant mutations C25672T (L94F), C25961T (T190I), and G26167T (V259L) in the ORF3a gene during the Thailand outbreaks. Complete genome sequencing can enhance the prediction of future variant changes in viral genomes, which is crucial to ensuring that vaccine strains are protective against worldwide outbreaks.
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Affiliation(s)
- Jiratchaya Puenpa
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand; (J.P.); (P.R.); (J.C.); (R.Y.)
| | - Vorthon Sawaswong
- Center of Excellence in Systems Microbiology, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand; (V.S.); (P.N.); (S.P.)
| | - Pattaraporn Nimsamer
- Center of Excellence in Systems Microbiology, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand; (V.S.); (P.N.); (S.P.)
| | - Sunchai Payungporn
- Center of Excellence in Systems Microbiology, Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand; (V.S.); (P.N.); (S.P.)
| | - Patthaya Rattanakomol
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand; (J.P.); (P.R.); (J.C.); (R.Y.)
| | - Nutsada Saengdao
- Department of Microbiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand; (N.S.); (K.S.)
| | - Jira Chansaenroj
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand; (J.P.); (P.R.); (J.C.); (R.Y.)
| | - Ritthideach Yorsaeng
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand; (J.P.); (P.R.); (J.C.); (R.Y.)
| | - Kamol Suwannakarn
- Department of Microbiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand; (N.S.); (K.S.)
| | - Yong Poovorawan
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand; (J.P.); (P.R.); (J.C.); (R.Y.)
- FRS(T), The Royal Society of Thailand, Sanam Sueapa, Dusit, Bangkok 10300, Thailand
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11
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Xie R, Edwards KM, Adam DC, Leung KSM, Tsang TK, Gurung S, Xiong W, Wei X, Ng DYM, Liu GYZ, Krishnan P, Chang LDJ, Cheng SMS, Gu H, Siu GKH, Wu JT, Leung GM, Peiris M, Cowling BJ, Poon LLM, Dhanasekaran V. Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong. Nat Commun 2023; 14:2422. [PMID: 37105966 PMCID: PMC10134727 DOI: 10.1038/s41467-023-38201-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 04/20/2023] [Indexed: 04/29/2023] Open
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.
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Affiliation(s)
- Ruopeng Xie
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kimberly M Edwards
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Dillon C Adam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kathy S M Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Tim K Tsang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shreya Gurung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Weijia Xiong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Xiaoman Wei
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Daisy Y M Ng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Gigi Y Z Liu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Pavithra Krishnan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Lydia D J Chang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Samuel M S Cheng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Haogao Gu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Gilman K H Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Joseph T Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Gabriel M Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Malik Peiris
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Benjamin J Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Leo L M Poon
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Vijaykrishna Dhanasekaran
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
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12
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Adam DC, Martín-Sánchez M, Gu H, Yang B, Lin Y, Wu P, Lau EH, Leung GM, Poon LL, Cowling BJ. Risk of within-hotel transmission of SARS-CoV-2 during on-arrival quarantine in Hong Kong: an epidemiological and phylogenomic investigation. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 33:100678. [PMID: 36643735 PMCID: PMC9825110 DOI: 10.1016/j.lanwpc.2022.100678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/23/2022] [Accepted: 12/19/2022] [Indexed: 01/08/2023]
Abstract
Background On-arrival quarantine has been one of the primary measures to prevent the introduction of SARS-CoV-2 into Hong Kong since the start of the pandemic. Most on-arrival quarantines have been done in hotels, with the duration of quarantine and testing frequency during quarantine modified over time along with other pandemic control measures. However, hotels are not designed with infection control in mind. We aimed to systematically study the potential risk of acquisition of SARS-CoV-2 infection among individuals undergoing hotel quarantine. Methods We examined data on each laboratory-confirmed COVID-19 case identified in on-arrival quarantine in a hotel in Hong Kong between 1 May 2020 and 31 January 2022. We sequenced the whole genomes of viruses from cases that overlapped with other confirmed cases in terms of the hotel of stay, date of arrival and date of testing positive. By combining multiple sources of evidence, we identify probable and plausible transmission events and calculate the overall risk of transmission. Findings Among 221 imported cases that overlapped with other cases detected during hotel quarantine with available sequence data, phylogenomic analyses identified five probable and two plausible clusters of within-hotel transmission. Only two of these clusters were recognised at the time. Including other clusters reported in Hong Kong, we estimate that 8-11 per 1000 cases identified in hotel quarantine may be infected by another unlinked case during quarantine, or 2-3 per 100,000 overseas arrivals. Interpretation We have identified additional undetected occurrences of COVID-19 transmission within hotel quarantine in Hong Kong. Although hotels provide suboptimal infection control as improvised quarantine facilities, the risk of contracting infection whilst in quarantine is low. However, these unlikely events could have high consequences by allowing the virus to spread into immunologically naïve communities. Additional vigilance should be taken in the absence of improved controls to identify such events. If on-arrival quarantine is expected to be used for a long time, quarantine facilities could be purpose-built to minimise the risk of transmission. Funding Health and Medical Research Fund, Hong Kong.
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Affiliation(s)
- Dillon C. Adam
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Mario Martín-Sánchez
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Haogao Gu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yun Lin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H.Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Gabriel M. Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Leo L.M. Poon
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong, China
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
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13
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Gu H, Quadeer AA, Krishnan P, Ng DYM, Chang LDJ, Liu GYZ, Cheng SMS, Lam TTY, Peiris M, McKay MR, Poon LLM. Within-host genetic diversity of SARS-CoV-2 lineages in unvaccinated and vaccinated individuals. Nat Commun 2023; 14:1793. [PMID: 37002233 PMCID: PMC10063955 DOI: 10.1038/s41467-023-37468-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
Viral and host factors can shape SARS-CoV-2 evolution. However, little is known about lineage-specific and vaccination-specific mutations that occur within individuals. Here, we analysed deep sequencing data from 2,820 SARS-CoV-2 respiratory samples with different viral lineages to describe the patterns of within-host diversity under different conditions, including vaccine-breakthrough infections. In unvaccinated individuals, variant of Concern (VOC) Alpha, Delta, and Omicron respiratory samples were found to have higher within-host diversity and were under neutral to purifying selection at the full genome level compared to non-VOC SARS-CoV-2. Breakthrough infections in 2-dose or 3-dose Comirnaty and CoronaVac vaccinated individuals did not increase levels of non-synonymous mutations and did not change the direction of selection pressure. Vaccine-induced antibody or T cell responses did not appear to have significant impact on within-host SARS-CoV-2 sequence diversification. Our findings suggest that vaccination does not increase exploration of SARS-CoV-2 protein sequence space and may not facilitate emergence of viral variants.
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Affiliation(s)
- Haogao Gu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Pavithra Krishnan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Daisy Y M Ng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Lydia D J Chang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Gigi Y Z Liu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Samuel M S Cheng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Tommy T Y Lam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Malik Peiris
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Matthew R McKay
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, 3010, Australia
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, 3000, Australia
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Leo L M Poon
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong SAR, China.
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
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14
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Evaluating Data Sharing of SARS-CoV-2 Genomes for Molecular Epidemiology across the COVID-19 Pandemic. Viruses 2023; 15:v15020560. [PMID: 36851774 PMCID: PMC9959893 DOI: 10.3390/v15020560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/12/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Following the emergence of COVID-19 in December 2019, caused by the coronavirus SARS-CoV-2, the disease spread dramatically worldwide. The use of genomics to trace the dissemination of the virus and the identification of novel variants was essential in defining measures for containing the disease. We aim to evaluate the global effort to genomically characterize the circulating lineages of SARS-CoV-2, considering the data deposited in GISAID, the major platform for data sharing in a massive worldwide collaborative undertaking. We contextualize data for nearly three years (January 2020-October 2022) for the major contributing countries, percentage of characterized isolates and time for data processing in the context of the global pandemic. Within this collaborative effort, we also evaluated the early detection of seven major SARS-CoV-2 lineages, G, GR, GH, GK, GV, GRY and GRA. While Europe and the USA, following an initial period, showed positive results across time in terms of cases sequenced and time for data deposition, this effort is heterogeneous worldwide. Given the current immunization the major threat is the appearance of variants that evade the acquired immunity. In that scenario, the monitoring of those hypothetical variants will still play an essential role.
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15
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Hao Y, Wang Y, Wang M, Zhou L, Shi J, Cao J, Wang D. The origins of COVID-19 pandemic: A brief overview. Transbound Emerg Dis 2022; 69:3181-3197. [PMID: 36218169 PMCID: PMC9874793 DOI: 10.1111/tbed.14732] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/26/2022] [Accepted: 10/04/2022] [Indexed: 02/06/2023]
Abstract
The novel coronavirus disease (COVID-19) outbreak that emerged at the end of 2019 has now swept the world for more than 2 years, causing immeasurable damage to the lives and economies of the world. It has drawn so much attention to discovering how the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated and entered the human body. The current argument revolves around two contradictory theories: a scenario of laboratory spillover events and human contact with zoonotic diseases. Here, we reviewed the transmission, pathogenesis, possible hosts, as well as the genome and protein structure of SARS-CoV-2, which play key roles in the COVID-19 pandemic. We believe the coronavirus was originally transmitted to human by animals rather than by a laboratory leak. However, there still needs more investigations to determine the source of the pandemic. Understanding how COVID-19 emerged is vital to developing global strategies for mitigating future outbreaks.
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Affiliation(s)
- Ying‐Jian Hao
- Key Laboratory of Cellular Physiology, Ministry of Education, Department of PhysiologyShanxi Medical UniversityTaiyuanChina
| | - Yu‐Lan Wang
- Key Laboratory of Cellular Physiology, Ministry of Education, Department of PhysiologyShanxi Medical UniversityTaiyuanChina
| | - Mei‐Yue Wang
- Key Laboratory of Cellular Physiology, Ministry of Education, Department of PhysiologyShanxi Medical UniversityTaiyuanChina
| | - Lan Zhou
- Key Laboratory of Cellular Physiology, Ministry of Education, Department of PhysiologyShanxi Medical UniversityTaiyuanChina
| | - Jian‐Yun Shi
- Key Laboratory of Cellular Physiology, Ministry of Education, Department of PhysiologyShanxi Medical UniversityTaiyuanChina
| | - Ji‐Min Cao
- Key Laboratory of Cellular Physiology, Ministry of Education, Department of PhysiologyShanxi Medical UniversityTaiyuanChina
| | - De‐Ping Wang
- Key Laboratory of Cellular Physiology, Ministry of Education, Department of PhysiologyShanxi Medical UniversityTaiyuanChina
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16
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COVID-19 pandemic fatigue and its sociodemographic and psycho-behavioral correlates: a population-based cross-sectional study in Hong Kong. Sci Rep 2022; 12:16114. [PMID: 36167729 PMCID: PMC9514690 DOI: 10.1038/s41598-022-19692-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 09/02/2022] [Indexed: 11/08/2022] Open
Abstract
Pandemic fatigue is a growing public health concern of the lingering COVID-19 pandemic. Despite its widespread mass media coverage, systematic empirical investigations are scarce. Under the Hong Kong Jockey Club SMART Family-Link Project, we conducted online and telephone surveys amid the pandemic in February to March 2021 to assess self-reported pandemic fatigue (range 0-10) in Hong Kong adults (N = 4726) and its associations with sociodemographic and psycho-behavioral (high vs low to moderate) variables. Data were weighted by sex, age, and education of the general population. Binary logistic regression models yielded adjusted odds ratios (aORs) for high pandemic fatigue (score ≥ 7) for sociodemographic and psycho-behavioral variables. 43.7% reported high pandemic fatigue. It was less common in older people (55-64 years: aOR 0.56, 95% CI 0.39-0.82; 65 + years: 0.33, 0.21-0.52) versus age group 18-24 years, but more common in those with tertiary education (1.36, 1.15-1.62) versus secondary or below. High pandemic fatigue was positively associated with depressive symptoms (aOR 1.83, 95% CI 1.55-2.17), anxiety symptoms (1.87, 1.58-2.20), loneliness (1.75, 1.32-2.31), personal fear of COVID-19 (2.61, 2.12-3.23), family fear of COVID-19 (2.03, 1.67-2.47), and current alcohol use (1.16, 1.00-1.33), but negatively associated with self-rated health (0.79, 0.68-0.92), personal happiness (0.63, 0.55-0.72), personal adversity coping capability (0.71, 0.63-0.81), family adversity coping capability (0.79, 0.69-0.90), family well-being (0.84, 0.73-0.97), family communication quality (0.86, 0.75-0.98), and frequent home exercise (0.82, 0.69-0.96; versus less frequent). We first used a single-item tool to measure COVID-19 pandemic fatigue, showing that it was common and associated with worse mental health, lower levels of personal and family well-being and alcohol use.
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17
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Gong WJ, Sit SMM, Lai AYK, Yu NX, Wang MP, Ho SY, Lam TH. Adversity coping capability and its associations with mental health and family wellbeing amid the COVID-19 pandemic in Hong Kong. BMC Psychiatry 2022; 22:553. [PMID: 35962361 PMCID: PMC9373882 DOI: 10.1186/s12888-022-04198-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 08/08/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Adversity coping capability (ACC) is important amid the COVID-19 pandemic. We examined the associations of ACC as measured by our one-item ACC scale (ACC-1) with mental health, family well-being and validity of ACC-1 in Hong Kong. METHODS A cross-sectional survey was conducted on Hong Kong Chinese adults aged ≥ 18 years by landline, mobile phone, and online survey from February to March 2021, when the fourth wave of COVID-19 was under control. ACC-1 consisted of the question: "How do you rate your capability to cope with adversities?" with higher scores (0-10) indicating stronger ACC. The associations of ACC with socioeconomic characteristics, resilience, mental health, and family wellbeing were examined by linear regression coefficients (βs). Data were weighted by sex, age, and education of the general population. RESULTS Of 7441 respondents, after weighing, 52.2% were female and 79.1% were aged 18 to 64 years. ACC-1 showed good construct validity, with higher ACC being associated with higher levels of resilience (adjusted β = 0.29), personal happiness (0.55), family happiness (0.42), family wellbeing (0.41), and family communication quality (0.41), and lower levels of depressive symptoms (-0.30), anxiety (-0.30), loneliness (-0.15); incremental validity with additional contributions of ACC to mental health and family wellbeing; and known-group validity with older age and favorable socioeconomic characteristics showing higher ACC (all P < 0.02). Females (mean ± standard deviation: 6.04 ± 1.82 vs 6.15 ± 1.96 [male]) and unemployed respondents (5.30 ± 1.99 vs 6.11 ± 2.03 [in paid employment]) had lower ACC (all P ≤ 0.02). CONCLUSIONS We have first shown that stronger ACC was associated with better mental health and family wellbeing, and the results support ACC-1 as a simple and valid measure of ACC.
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Affiliation(s)
- Wei Jie Gong
- grid.263488.30000 0001 0472 9649Department of General Practice, Health Science Center, Shenzhen University, Shenzhen, China ,grid.194645.b0000000121742757School of Public Health, The University of Hong Kong, Hong Kong, China
| | - Shirley Man Man Sit
- grid.194645.b0000000121742757School of Public Health, The University of Hong Kong, Hong Kong, China ,grid.194645.b0000000121742757School of Nursing, The University of Hong Kong, Hong Kong, China
| | - Agnes Yuen Kwan Lai
- grid.194645.b0000000121742757School of Nursing, The University of Hong Kong, Hong Kong, China
| | - Nancy Xiaonan Yu
- grid.35030.350000 0004 1792 6846Department of Social and Behavioral Sciences, City University of Hong Kong, Hong Kong, China
| | - Man Ping Wang
- School of Nursing, The University of Hong Kong, Hong Kong, China.
| | - Sai Yin Ho
- grid.194645.b0000000121742757School of Public Health, The University of Hong Kong, Hong Kong, China
| | - Tai Hing Lam
- grid.194645.b0000000121742757School of Public Health, The University of Hong Kong, Hong Kong, China
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18
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Gu H, Quadeer AA, Krishnan P, Ng DY, Chang LD, Liu GY, Cheng SS, Lam TT, Peiris M, McKay MR, Poon LL. Within-host diversity of SARS-CoV-2 lineages and effect of vaccination. RESEARCH SQUARE 2022:rs.3.rs-1927944. [PMID: 35982671 PMCID: PMC9387541 DOI: 10.21203/rs.3.rs-1927944/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Viral and host factors can shape SARS-CoV-2 within-host viral diversity and virus evolution. However, little is known about lineage-specific and vaccination-specific mutations that occur within individuals. Here we analysed deep sequencing data from 2,146 SARS-CoV-2 samples with different viral lineages to describe the patterns of within-host diversity in different conditions, including vaccine-breakthrough infections. Variant of Concern (VOC) Alpha, Delta, and Omicron samples were found to have higher within-host nucleotide diversity while being under weaker purifying selection at full genome level compared to non-VOC SARS-CoV-2 viruses. Breakthrough Delta and Omicron infections in Comirnaty and CoronaVac vaccinated individuals appeared to have higher within-host purifying selection at the full-genome and/or Spike gene levels. Vaccine-induced antibody or T cell responses did not appear to have significant impact on within-host SARS-CoV-2 evolution. Our findings suggest that vaccination does not increase SARS-CoV-2 protein sequence space and may not facilitate emergence of more viral variants.
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Affiliation(s)
- Haogao Gu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ahmed Abdul Quadeer
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Pavithra Krishnan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Daisy Y.M. Ng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Lydia D.J Chang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Gigi Y.Z. Liu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Samuel S.M. Cheng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Tommy T.Y. Lam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Malik Peiris
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Matthew R. McKay
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Leo L.M. Poon
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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19
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Sun W, Cheng SSM, Lam KNT, Kwan TC, Wong RWK, Lau LHK, Liu GYZ, Luk LLH, Li JKC, Gu H, Peiris M, Poon LLM. Natural Reassortment of Eurasian Avian-Like Swine H1N1 and Avian H9N2 Influenza Viruses in Pigs, China. Emerg Infect Dis 2022; 28:1509-1512. [PMID: 35731193 PMCID: PMC9239857 DOI: 10.3201/eid2807.220642] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Several zoonotic influenza A viruses detected in humans contain genes derived from avian H9N2 subtypes. We uncovered a Eurasian avian-like H1N1 swine influenza virus with polymerase basic 1 and matrix gene segments derived from the H9N2 subtype, suggesting that H9N2 viruses are infecting pigs and reassorting with swine influenza viruses in China.
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20
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Moodley K. The ethics behind mandatory COVID-19 vaccination post-Omicron: The South African context. S AFR J SCI 2022. [DOI: 10.17159/sajs.2022/13239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
The legitimacy of mandatory vaccine policies is underscored by a public health ethics framework based on the principles of limited autonomy, social justice and the common good. Ideally, vaccine uptake ought to occur on a voluntary basis as an act of solidarity to ensure that everyone is protected. Given that the altruistic approach has failed and vaccine uptake remains sub-optimal in South Africa, in this paper, I argue for vaccine mandates, in a post-Omicron context. This viewpoint is substantiated by several considerations. Healthcare workers are fatigued after 2 years of treating COVID-19 and many are still treating patients with post-viral syndromes, mental health conditions and cardiovascular complications. Health systems remain under pressure as people with non-COVID diseases, neglected during the pandemic, are also now presenting to medical practices and hospitals. Although South Africa has emerged from a relatively less severe fourth wave of COVID-19, there have been many deaths. Vaccine and natural immunity in a relatively young general population has been advantageous. However, the country has a high prevalence of HIV and those who are untreated may not be able to clear the coronavirus easily. Similarly chronic illnesses place many at risk for severe disease from COVID variants, especially if unvaccinated. The future is shrouded in uncertainty. The next variant could be similar to or less severe than Omicron, yet still impact negatively on health systems, education and the economy. Physical distancing is not ideal in many low socio-economic settings, making vaccines an important component of our prevention toolbox. Our safest option now is to ensure that as many South Africans as possible are vaccinated and receive boosters. Vaccine mandates work to achieve this end.
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Affiliation(s)
- Keymanthri Moodley
- Centre for Medical Ethics and Law, Department of Medicine, Stellenbosch University, Cape Town, South Africa
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21
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Lin Y, Yang B, Cobey S, Lau EHY, Adam DC, Wong JY, Bond HS, Cheung JK, Ho F, Gao H, Ali ST, Leung NHL, Tsang TK, Wu P, Leung GM, Cowling BJ. Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission. Nat Commun 2022; 13:1155. [PMID: 35241662 PMCID: PMC8894407 DOI: 10.1038/s41467-022-28812-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/14/2022] [Indexed: 12/20/2022] Open
Abstract
Many locations around the world have used real-time estimates of the time-varying effective reproductive number (\documentclass[12pt]{minimal}
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\begin{document}$${R}_{t}$$\end{document}Rt) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of \documentclass[12pt]{minimal}
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\begin{document}$${R}_{t}$$\end{document}Rt are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the latent period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of \documentclass[12pt]{minimal}
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\begin{document}$${R}_{t}$$\end{document}Rt based on case counts. We demonstrate that cycle threshold values could be used to improve real-time \documentclass[12pt]{minimal}
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\begin{document}$${R}_{t}$$\end{document}Rt estimation, enabling more timely tracking of epidemic dynamics. The time-varying effective reproductive number (Rt) is useful for monitoring transmission of infections such as COVID-19, but reporting delays impact case count-based estimation methods. Here, the authors demonstrate and validate a method for estimation of Rt based on viral load data from Hong Kong that does not require accurate daily counts.
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Affiliation(s)
- Yun Lin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Dillon C Adam
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jessica Y Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Helen S Bond
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Justin K Cheung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Faith Ho
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Huizhi Gao
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Nancy H L Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China. .,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China.
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