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Yuan J, Xu Y, Wong IOL, Lam WWT, Ni MY, Cowling BJ, Liao Q. Dynamic predictors of COVID-19 vaccination uptake and their interconnections over two years in Hong Kong. Nat Commun 2024; 15:290. [PMID: 38177142 PMCID: PMC10767005 DOI: 10.1038/s41467-023-44650-9] [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: 09/04/2023] [Accepted: 12/20/2023] [Indexed: 01/06/2024] Open
Abstract
The global rollout of COVID-19 vaccines faces a significant barrier in the form of vaccine hesitancy. This study adopts a dynamic and network perspective to explore the determinants of COVID-19 vaccine uptake in Hong Kong, focusing on multi-level determinants and their interconnections. Following the framework proposed by the Strategic Advisory Group of Experts (SAGE), the study used repeated cross-sectional surveys to map these determinants at multiple levels and investigates their interconnections simultaneously in a sample of 15,179 over two years. The results highlight the dynamic nature of COVID-19 vaccine hesitancy in an evolving pandemic. The findings suggest that vaccine confidence attitudes play crucial roles in vaccination uptake, with their importance shifting over time. The initial emphasis on vaccine safety gradually transitioned to heightened consideration of vaccine effectiveness at a later stage. The study also highlights the impact of chronic condition, age, COVID-19 case numbers, and non-pharmaceutical preventive behaviours on vaccine uptake. Higher educational attainment and being married were associated with primary and booster vaccine uptake and it may be possible to leverage these groups as early innovation adopters. Trust in government acts as a crucial bridging factor linking various variables in the networks with vaccine confidence attitudes, which subsequently closely linked to vaccine uptake. This study provides insights for designing future effective vaccination programmes for changing circumstances.
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Affiliation(s)
- Jiehu Yuan
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Yucan Xu
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Irene Oi Ling 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
| | - Wendy Wing Tak Lam
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Li Ka Shing Faculty of Medicine, Jocky Club Institute of Cancer Care, The University of Hong Kong, Hong Kong, China
| | - Michael Y Ni
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
- Urban Systems Institute, The University of Hong Kong, 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.
| | - Qiuyan Liao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
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Moore H, Hill B, Emery J, Gussy M, Siriwardena AN, Spaight R, Tanser F. An early warning precision public health approach for assessing COVID-19 vulnerability in the UK: the Moore-Hill Vulnerability Index (MHVI). BMC Public Health 2023; 23:2147. [PMID: 37919728 PMCID: PMC10623819 DOI: 10.1186/s12889-023-17092-7] [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: 03/29/2023] [Accepted: 10/28/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND Most COVID-19 vulnerability indices rely on measures that are biased by rates of exposure or are retrospective like mortality rates that offer little opportunity for intervention. The Moore-Hill Vulnerability Index (MHVI) is a precision public health early warning alternative to traditional infection fatality rates that presents avenues for mortality prevention. METHODS We produced an infection-severity vulnerability index by calculating the proportion of all recorded positive cases that were severe and attended by ambulances at small area scale for the East Midlands of the UK between May 2020 and April 2022. We produced maps identifying regions with high and low vulnerability, investigated the accuracy of the index over shorter and longer time periods, and explored the utility of the MHVI compared to other common proxy measures and indices. Analysis included exploring the correlation between our novel index and the Index of Multiple Deprivation (IMD). RESULTS The MHVI captures geospatial dynamics that single metrics alone often overlook, including the compound health challenges associated with disadvantaged and declining coastal towns inhabited by communities with post-industrial health legacies. A moderate negative correlation between MHVI and IMD reflects spatial analysis which suggests that high vulnerability occurs in affluent rural as well as deprived coastal and urban communities. Further, the MHVI estimates of severity rates are comparable to infection fatality rates for COVID-19. CONCLUSIONS The MHVI identifies regions with known high rates of poor health outcomes prior to the pandemic that case rates or mortality rates alone fail to identify. Pre-hospital early warning measures could be utilised to prevent mortality during a novel pandemic.
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Affiliation(s)
- Harriet Moore
- Department of Geography, University of Lincoln, Lincoln, United Kingdom
- Development, Inequalities, Resilience and Environments Research Group, Lincoln, United Kingdom
- EDGE Consortium, Lincoln, Ontario, United Kingdom, Canada
| | - Bartholomew Hill
- EDGE Consortium, Lincoln, Ontario, United Kingdom, Canada
- WATERWISER/WEDC, Loughborough University, Loughborough, United Kingdom
| | - Jay Emery
- Department of Geography, University of Lincoln, Lincoln, United Kingdom
- Development, Inequalities, Resilience and Environments Research Group, Lincoln, United Kingdom
| | - Mark Gussy
- EDGE Consortium, Lincoln, Ontario, United Kingdom, Canada
- Lincoln International Institute for Rural Health, Lincoln, United Kingdom
| | - Aloysius Niroshan Siriwardena
- EDGE Consortium, Lincoln, Ontario, United Kingdom, Canada
- Community and Health Research Unit, School of Health and Social Care, University of Lincoln, Lincoln, United Kingdom
| | - Robert Spaight
- EDGE Consortium, Lincoln, Ontario, United Kingdom, Canada
- Community and Health Research Unit, School of Health and Social Care, University of Lincoln, Lincoln, United Kingdom
- East Midlands Ambulance Service NHS Trust, Nottingham, England
| | - Frank Tanser
- EDGE Consortium, Lincoln, Ontario, United Kingdom, Canada
- School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
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Yuan J, Dong M, Wong IOL, Cowing BJ, Lam WWT, Ni MY, Liao Q. Can psychological distress account for the associations between COVID-19 vaccination acceptance and socio-economic vulnerability? Appl Psychol Health Well Being 2023; 15:1583-1602. [PMID: 37142547 DOI: 10.1111/aphw.12452] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 04/24/2023] [Indexed: 05/06/2023]
Abstract
Socially disadvantaged individuals and communities consistently showed lower COVID-19 vaccination acceptance. We aimed to examine the psychological mechanisms that could explain such vaccination disparities. This study used data from serial population-based surveys conducted since the COVID-19 vaccination programme being launched in Hong Kong (N = 28,734). We first assessed the correlations of community-level and individual-level social vulnerability with COVID-19 vaccination acceptance. Structural equation modelling (SEM) was then conducted to test whether psychological distress measured by PHQ-4 can account for the associations between participants' socio-economic vulnerability and COVID-19 vaccination acceptance. The third part analysis examined whether perceived negativity of vaccine-related news and affect towards COVID-19 vaccines accounted for the association between psychological distress and COVID-19 vaccination. Communities with higher social vulnerability scores and participants who had more vulnerable socio-economic status showed lower COVID-19 vaccination acceptance. Individuals with more vulnerable socio-economic status reported higher psychological distress, which lowered COVID-19 vaccination acceptance. Furthermore, higher psychological distress was associated with lower vaccination acceptance through its psychological mechanisms of processing vaccine-related information. We proposed a renewed focus on tackling psychological distress rather than merely increasing vaccine accessibility in more socio-economic-disadvantaged groups for promoting COVID-19 vaccination acceptance.
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Affiliation(s)
- Jiehu Yuan
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Meihong Dong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Irene Oi Ling Wong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- World Health Organization Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Benjamin J Cowing
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- World Health Organization Collaborating Center 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, Hong Kong Science and Technology Park, Hong Kong, China
| | - Wendy Wing Tak Lam
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Li Ka Shing Faculty of Medicine, Jockey Club Institute of Cancer Care, The University of Hong Kong, Hong Kong, China
| | - Michael Y Ni
- World Health Organization Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Hong Kong, China
| | - Qiuyan Liao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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Alidadi M, Sharifi A, Murakami D. Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city. SUSTAINABLE CITIES AND SOCIETY 2023; 97:104743. [PMID: 37397232 PMCID: PMC10304317 DOI: 10.1016/j.scs.2023.104743] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023]
Abstract
This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID-19 cases were concentrated in central Tokyo, with clustering levels decreasing after the outbreaks. COVID-19 infection rates were higher in areas with a greater density of retail stores, restaurants, health facilities, workers in those sectors, public transit use, and telecommuting. However, household crowding was negatively associated. The study also found that telecommuting rate and housing crowding were the strongest predictors of COVID-19 infection rates in Tokyo, according to the regression model with time-fixed effects, which had the best validation and stability. This study's results could be useful for researchers and policymakers, particularly because Japan and Tokyo have unique circumstances, as there was no mandatory lockdown during the pandemic.
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Affiliation(s)
- Mehdi Alidadi
- Centre for Urban Research, School of Global, Urban and Social Studies, RMIT University, Melbourne, Australia
- Hiroshima University, Graduate School of Engineering and Advanced Science, Hiroshima, Japan
| | - Ayyoob Sharifi
- Hiroshima University, The IDEC Institute and Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima, Japan
| | - Daisuke Murakami
- The Institute of Statistical Mathematics, Department of Statistical Data Science, Tokyo, Japan
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Liao Q, Dong M, Yuan J, Lam WWT, Fielding R. Community vulnerability to the COVID-19 pandemic: A narrative synthesis from an ecological perspective. J Glob Health 2022; 12:05054. [PMID: 36462204 PMCID: PMC9719409 DOI: 10.7189/jogh.12.05054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Background We aimed to conduct a narrative synthesis of components and indicators of community vulnerability to a pandemic and discuss their interrelationships from an ecological perspective. Methods We searched from PubMed, Embase, Web of Science, PsycINFO, and Scopus (updated to November 2021) for studies focusing on community vulnerability to a pandemic caused by novel respiratory viruses on a geographic unit basis . Studies that reported the associations of community vulnerability levels with at least one disease morbidity or mortality outcome were included. Results Forty-one studies were included. All were about the COVID-19 pandemic. Suitable temperature and humidity environments, advanced social and human development (including high population density and human mobility, connectivity, and occupations), and settings that intensified physical interactions are important indicators of vulnerability to viral exposure. However, the eventual pandemic health impacts are predominant in communities that faced environmental pollution, higher proportions of socioeconomically deprived people, health deprivation, higher proportions of poor-condition households, limited access to preventive health care and urban infrastructure, uneven social and human development, and racism. More stringent social distancing policies were associated with lower COVID-19 morbidity and mortality only in the early pandemic phases. Prolonged social distancing policies can disproportionately burden the socially disadvantaged and racially/ethnically marginalized groups. Conclusions Community vulnerability to a pandemic is foremost the vulnerability of the ecological systems shaped by complex interactions between the human and environmental systems. Registration PROSPERO (CRD42021266186).
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Alidadi M, Sharifi A. Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 850:158056. [PMID: 35985590 PMCID: PMC9383943 DOI: 10.1016/j.scitotenv.2022.158056] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 05/25/2023]
Abstract
Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.
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Affiliation(s)
- Mehdi Alidadi
- Graduate School of Engineering and Advanced Sciences, Hiroshima University, Hiroshima, Japan.
| | - Ayyoob Sharifi
- Graduate School of Humanities and Social Science, Network for Education and Research on Peace and Sustainability (NERPS), and the Center for Peaceful and Sustainable Futures (CEPEAS), Hiroshima University, Japan.
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Beese F, Waldhauer J, Wollgast L, Pförtner TK, Wahrendorf M, Haller S, Hoebel J, Wachtler B. Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic—A Scoping Review. Int J Public Health 2022; 67:1605128. [PMID: 36105178 PMCID: PMC9464808 DOI: 10.3389/ijph.2022.1605128] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/16/2022] [Indexed: 01/04/2023] Open
Abstract
Objectives: International evidence of socioeconomic inequalities in COVID-19 outcomes is extensive and growing, but less is known about the temporal dynamics of these inequalities over the course of the pandemic. Methods: We systematically searched the Embase and Scopus databases. Additionally, several relevant journals and the reference lists of all included articles were hand-searched. This study follows the PRISMA guidelines for scoping reviews. Results: Forty-six studies were included. Of all analyses, 91.4% showed stable or increasing socioeconomic inequalities in COVID-19 outcomes over the course of the pandemic, with socioeconomically disadvantaged populations being most affected. Furthermore, the study results showed temporal dynamics in socioeconomic inequalities in COVID-19, frequently initiated through higher COVID-19 incidence and mortality rates in better-off populations and subsequent crossover dynamics to higher rates in socioeconomically disadvantaged populations (41.9% of all analyses). Conclusion: The identified temporal dynamics of socioeconomic inequalities in COVID-19 outcomes have relevant public health implications. Socioeconomic inequalities should be monitored over time to enable the adaption of prevention and interventions according to the social particularities of specific pandemic phases.
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Affiliation(s)
- Florian Beese
- Division of Social Determinants of Health, Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany
- *Correspondence: Florian Beese,
| | - Julia Waldhauer
- Division of Social Determinants of Health, Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany
| | - Lina Wollgast
- Division of Social Determinants of Health, Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany
| | - Timo-Kolja Pförtner
- Institute of Medical Sociology, Health Services Research and Rehabilitation Science, Faculty of Medicine and Faculty of Human Sciences, University of Cologne, Cologne, Germany
- Research Methods Division, Faculty of Human Sciences, University of Cologne, Cologne, Germany
| | - Morten Wahrendorf
- Institute of Medical Sociology, Centre for Health and Society (CHS), Medical Faculty, Heinrich-Heine University, Dusseldorf, Germany
| | - Sebastian Haller
- Division of Healthcare-Associated Infections, Surveillance of Antibiotic Resistance and Consumption, Department of Infectious Disease Epidemiology, Robert Koch Institute, Berlin, Germany
| | - Jens Hoebel
- Division of Social Determinants of Health, Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany
| | - Benjamin Wachtler
- Division of Social Determinants of Health, Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany
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Gu H, Xie R, Adam DC, Tsui JLH, Chu DK, Chang LDJ, Cheuk SSY, Gurung S, Krishnan P, Ng DYM, Liu GYZ, Wan CKC, Cheng SSM, Edwards KM, Leung KSM, Wu JT, Tsang DNC, Leung GM, Cowling BJ, Peiris M, Lam TTY, Dhanasekaran V, Poon LLM. Genomic epidemiology of SARS-CoV-2 under an elimination strategy in Hong Kong. Nat Commun 2022; 13:736. [PMID: 35136039 PMCID: PMC8825829 DOI: 10.1038/s41467-022-28420-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 01/19/2022] [Indexed: 12/15/2022] Open
Abstract
Hong Kong employed a strategy of intermittent public health and social measures alongside increasingly stringent travel regulations to eliminate domestic SARS-CoV-2 transmission. By analyzing 1899 genome sequences (>18% of confirmed cases) from 23-January-2020 to 26-January-2021, we reveal the effects of fluctuating control measures on the evolution and epidemiology of SARS-CoV-2 lineages in Hong Kong. Despite numerous importations, only three introductions were responsible for 90% of locally-acquired cases. Community outbreaks were caused by novel introductions rather than a resurgence of circulating strains. Thus, local outbreak prevention requires strong border control and community surveillance, especially during periods of less stringent social restriction. Non-adherence to prolonged preventative measures may explain sustained local transmission observed during wave four in late 2020 and early 2021. We also found that, due to a tight transmission bottleneck, transmission of low-frequency single nucleotide variants between hosts is rare.
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Affiliation(s)
- Haogao Gu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ruopeng Xie
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Dillon C Adam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Joseph L-H Tsui
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Daniel K Chu
- 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
| | - Sammi S Y Cheuk
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Shreya Gurung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, 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
| | - Gigi Y Z Liu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Carrie K C Wan
- 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
| | - Kimberly M Edwards
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Kathy S M Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Joseph T Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Dominic N C Tsang
- Centre for Health Protection, Department of Health, The Government of Hong Kong Special Administrative Region, Hong Kong, China
| | - Gabriel M Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Benjamin J Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, 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
- 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
| | - Tommy T Y Lam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong, China
| | - Vijaykrishna Dhanasekaran
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
| | - Leo L M Poon
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, 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.
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