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Chen Z, Deng X, Fang L, Sun K, Wu Y, Che T, Zou J, Cai J, Liu H, Wang Y, Wang T, Tian Y, Zheng N, Yan X, Sun R, Xu X, Zhou X, Ge S, Liang Y, Yi L, Yang J, Zhang J, Ajelli M, Yu H. Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: A descriptive study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2022; 29:100592. [PMID: 36090701 PMCID: PMC9448412 DOI: 10.1016/j.lanwpc.2022.100592] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Background In early March 2022, a major outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant spread rapidly throughout Shanghai, China. Here we aimed to provide a description of the epidemiological characteristics and spatiotemporal transmission dynamics of the Omicron outbreak under the population-based screening and lockdown policies implemented in Shanghai. Methods We extracted individual information on SARS-CoV-2 infections reported between January 1 and May 31, 2022, and on the timeline of the adopted non-pharmaceutical interventions. The epidemic was divided into three phases: i) sporadic infections (January 1-February 28), ii) local transmission (March 1-March 31), and iii) city-wide lockdown (April 1 to May 31). We described the epidemic spread during these three phases and the subdistrict-level spatiotemporal distribution of the infections. To evaluate the impact on the transmission of SARS-CoV-2 of the adopted targeted interventions in Phase 2 and city-wide lockdown in Phase 3, we estimated the dynamics of the net reproduction number (Rt ). Findings A surge in imported infections in Phase 1 triggered cryptic local transmission of the Omicron variant in early March, resulting in the largest outbreak in mainland China since the original wave. A total of 626,000 SARS-CoV-2 infections were reported in 99.5% (215/216) of the subdistricts of Shanghai until the end of May. The spatial distribution of the infections was highly heterogeneous, with 37% of the subdistricts accounting for 80% of all infections. A clear trend from the city center towards adjacent suburban and rural areas was observed, with a progressive slowdown of the epidemic spread (from 463 to 244 meters/day) prior to the citywide lockdown. During Phase 2, Rt remained well above 1 despite the implementation of multiple targeted interventions. The citywide lockdown imposed on April 1 led to a marked decrease in transmission, bringing Rt below the epidemic threshold in the entire city on April 14 and ultimately leading to containment of the outbreak. Interpretation Our results highlight the risk of widespread outbreaks in mainland China, particularly under the heightened pressure of imported infections. The targeted interventions adopted in March 2022 were not capable of halting transmission, and the implementation of a strict, prolonged city-wide lockdown was needed to successfully contain the outbreak, highlighting the challenges for containing Omicron outbreaks. Funding Key Program of the National Natural Science Foundation of China (82130093); Shanghai Rising-Star Program (22QA1402300).
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Affiliation(s)
- Zhiyuan Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiaowei Deng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Liqun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Yanpeng Wu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Tianle Che
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Junyi Zou
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jun Cai
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Hengcong Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Tao Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Yuyang Tian
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Nan Zheng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xuemei Yan
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Ruijia Sun
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiangyanyu Xu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiaoyu Zhou
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Shijia Ge
- Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxia Liang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Lan Yi
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Juan Yang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
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Tong C, Shi W, Siu GKH, Zhang A, Shi Z. Understanding spatiotemporal symptom onset risk of Omicron BA.1, BA.2 and hamster-related Delta AY.127. Front Public Health 2022; 10:978052. [PMID: 36187667 PMCID: PMC9523538 DOI: 10.3389/fpubh.2022.978052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 08/22/2022] [Indexed: 01/25/2023] Open
Abstract
Purpose Investigation of the community-level symptomatic onset risk regarding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern, is crucial to the pandemic control in the new normal. Methods Investigated in this study is the spatiotemporal symptom onset risk with Omicron BA.1, BA.2, and hamster-related Delta AY.127 by a joint analysis of community-based human mobility, virus genomes, and vaccinations in Hong Kong. Results The spatial spread of Omicron BA.2 was found to be 2.91 times and 2.56 times faster than that of Omicron BA.1 and Delta AY.127. Identified has been an early spatial invasion process in which spatiotemporal symptom onset risk was associated with intercommunity and cross-community human mobility of a dominant source location, especially regarding enhancement of the effects of the increased intrinsic transmissibility of Omicron BA.2. Further explored is the spread of Omicron BA.1, BA.2, and Delta AY.127 under different full and booster vaccination rate levels. An increase in full vaccination rates has primarily contributed to the reduction in areas within lower onset risk. An increase in the booster vaccination rate can promote a reduction in those areas within higher onset risk. Conclusions This study has provided a comprehensive investigation concerning the spatiotemporal symptom onset risk of Omicron BA.1, BA.2, and hamster-related Delta AY.127, and as such can contribute some help to countries and regions regarding the prevention of the emergence of such as these variants, on a strategic basis. Moreover, this study provides scientifically derived findings on the impact of full and booster vaccination campaigns working in the area of the reduction of symptomatic infections.
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Affiliation(s)
- Chengzhuo Tong
- Department of Land Surveying and Geo-Informatics, Otto Poon Charitable Foundation Smart Cities Research Institute, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Wenzhong Shi
- Department of Land Surveying and Geo-Informatics, Otto Poon Charitable Foundation Smart Cities Research Institute, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China,*Correspondence: Wenzhong Shi
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Anshu Zhang
- Department of Land Surveying and Geo-Informatics, Otto Poon Charitable Foundation Smart Cities Research Institute, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Zhicheng Shi
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, China,Zhicheng Shi
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Chen Z, Deng X, Fang L, Sun K, Wu Y, Che T, Zou J, Cai J, Liu H, Wang Y, Wang T, Tian Y, Zheng N, Yan X, Sun R, Xu X, Zhou X, Ge S, Liang Y, Yi L, Yang J, Zhang J, Ajelli M, Yu H. Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: a descriptive study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.06.11.22276273. [PMID: 35765564 PMCID: PMC9238184 DOI: 10.1101/2022.06.11.22276273] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Background In early March 2022, a major outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant spread rapidly throughout Shanghai, China. Here we aimed to provide a description of the epidemiological characteristics and spatiotemporal transmission dynamics of the Omicron outbreak under the population-based screening and lockdown policies implemented in Shanghai. Methods We extracted individual information on SARS-CoV-2 infections reported between January 1 and May 31, 2022, and on the timeline of the adopted non-pharmacological interventions. The epidemic was divided into three phases: i) sporadic infections (January 1-February 28), ii) local transmission (March 1-March 31), and iii) city-wide lockdown (April 1 to May 31). We described the epidemic spread during these three phases and the subdistrict-level spatiotemporal distribution of the infections. To evaluate the impact on the transmission of SARS-CoV-2 of the adopted targeted interventions in Phase 2 and city-wide lockdown in Phase 3, we estimated the dynamics of the net reproduction number ( R t ). Findings A surge in imported infections in Phase 1 triggered cryptic local transmission of the Omicron variant in early March, resulting in the largest coronavirus disease 2019 (COVID-19) outbreak in mainland China since the original wave. A total of 626,000 SARS-CoV-2 infections were reported in 99.5% (215/216) of the subdistricts of Shanghai. The spatial distribution of the infections was highly heterogeneous, with 40% of the subdistricts accounting for 80% of all infections. A clear trend from the city center towards adjacent suburban and rural areas was observed, with a progressive slowdown of the epidemic spread (from 544 to 325 meters/day) prior to the citywide lockdown. During Phase 2, R t remained well above 1 despite the implementation of multiple targeted interventions. The citywide lockdown imposed on April 1 led to a marked decrease in transmission, bringing R t below the epidemic threshold in the entire city on April 14 and ultimately leading to containment of the outbreak. Interpretation Our results highlight the risk of widespread outbreaks in mainland China, particularly under the heightened pressure of imported infections. The targeted interventions adopted in March 2022 were not capable of halting transmission, and the implementation of a strict, prolonged city-wide lockdown was needed to successfully contain the outbreak, highlighting the challenges for successfully containing Omicron outbreaks. Funding Key Program of the National Natural Science Foundation of China (82130093). Research in context Evidence before this study: On May 24, 2022, we searched PubMed and Europe PMC for papers published or posted on preprint servers after January 1, 2022, using the following query: ("SARS-CoV-2" OR "Omicron" OR "BA.2") AND ("epidemiology" OR "epidemiological" OR "transmission dynamics") AND ("Shanghai"). A total of 26 studies were identified; among them, two aimed to describe or project the spread of the 2022 Omicron outbreak in Shanghai. One preprint described the epidemiological and clinical characteristics of 376 pediatric SARS-CoV-2 infections in March 2022, and the other preprint projected the epidemic progress in Shanghai, without providing an analysis of field data. In sum, none of these studies provided a comprehensive description of the epidemiological characteristics and spatiotemporal transmission dynamics of the outbreak.Added value of this study: We collected individual information on SARS-CoV-2 infection and the timeline of the public health response. Population-based screenings were repeatedly implemented during the outbreak, which allowed us to investigate the spatiotemporal spread of the Omicron BA.2 variant as well as the impact of the implemented interventions, all without enduring significant amounts of underreporting from surveillance systems, as experienced in other areas. This study provides the first comprehensive assessment of the Omicron outbreak in Shanghai, China.Implications of all the available evidence: This descriptive study provides a comprehensive understanding of the epidemiological features and transmission dynamics of the Omicron outbreak in Shanghai, China. The empirical evidence from Shanghai, which was ultimately able to curtail the outbreak, provides invaluable information to policymakers on the impact of the containment strategies adopted by the Shanghai public health officials to prepare for potential outbreaks caused by Omicron or novel variants.
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Affiliation(s)
- Zhiyuan Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiaowei Deng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Liqun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Yanpeng Wu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Tianle Che
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Junyi Zou
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jun Cai
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Hengcong Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Tao Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Yuyang Tian
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Nan Zheng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xuemei Yan
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Ruijia Sun
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiangyanyu Xu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiaoyu Zhou
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Shijia Ge
- Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxiang Liang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Lan Yi
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Juan Yang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
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Zhang A, Shi W, Tong C, Zhu X, Liu Y, Liu Z, Yao Y, Shi Z. The fine-scale associations between socioeconomic status, density, functionality, and spread of COVID-19 within a high-density city. BMC Infect Dis 2022; 22:274. [PMID: 35313829 PMCID: PMC8936044 DOI: 10.1186/s12879-022-07274-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 03/14/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Motivated by the need for precise epidemic control and epidemic-resilient urban design, this study aims to reveal the joint and interactive associations between urban socioeconomic, density, connectivity, and functionality characteristics and the COVID-19 spread within a high-density city. Many studies have been made on the associations between urban characteristics and the COVID-19 spread, but there is a scarcity of such studies in the intra-city scale and as regards complex joint and interactive associations by using advanced machine learning approaches. METHODS Differential-evolution-based association rule mining was used to investigate the joint and interactive associations between the urban characteristics and the spatiotemporal distribution of COVID-19 confirmed cases, at the neighborhood scale in Hong Kong. The associations were comparatively studied for the distribution of the cases in four waves of COVID-19 transmission: before Jun 2020 (wave 1 and 2), Jul-Oct 2020 (wave 3), and Nov 2020-Feb 2021 (wave 4), and for local and imported confirmed cases. RESULTS The first two waves of COVID-19 were found mainly characterized by higher-socioeconomic-status (SES) imported cases. The third-wave outbreak concentrated in densely populated and usually lower-SES neighborhoods, showing a high risk of within-neighborhood virus transmissions jointly contributed by high density and unfavorable SES. Starting with a super-spread which considerably involved high-SES population, the fourth-wave outbreak showed a stronger link to cross-neighborhood transmissions driven by urban functionality. Then the outbreak diffused to lower-SES neighborhoods and interactively aggravated the within-neighborhood pandemic transmissions. Association was also found between a higher SES and a slightly longer waiting period (i.e., the period from symptom onset to diagnosis of symptomatic cases), which further indicated the potential contribution of higher-SES population to the pandemic transmission. CONCLUSIONS The results of this study may provide references to developing precise anti-pandemic measures for specific neighborhoods and virus transmission routes. The study also highlights the essentiality of reliving co-locating overcrowdedness and unfavorable SES for developing epidemic-resilient compact cities, and the higher obligation of higher-SES population to conform anti-pandemic policies.
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Affiliation(s)
- Anshu Zhang
- Otto Poon Charitable Foundation Smart Cities Research Institute, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Wenzhong Shi
- Otto Poon Charitable Foundation Smart Cities Research Institute, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
| | - Chengzhuo Tong
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Xiaosheng Zhu
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Yijia Liu
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Zhewei Liu
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Yepeng Yao
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Zhicheng Shi
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, China
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Yuan HY, Blakemore C. The impact of contact tracing and testing on controlling COVID-19 outbreak without lockdown in Hong Kong: An observational study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2022; 20:100374. [PMID: 35072128 PMCID: PMC8759949 DOI: 10.1016/j.lanwpc.2021.100374] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 11/20/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022]
Affiliation(s)
- Hsiang-Yu Yuan
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
- Centre for Applied One Health Research and Policy Advice, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
- Corresponding author.
| | - Colin Blakemore
- Department of Neuroscience, City University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute for Advanced Study, City University of Hong Kong, Hong Kong SAR, China
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In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES, INDIA. SECTION B 2022; 92:81-87. [PMID: 35035034 PMCID: PMC8741561 DOI: 10.1007/s40011-021-01292-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/09/2021] [Accepted: 09/02/2021] [Indexed: 12/13/2022]
Abstract
The novel coronavirus strain SARS-CoV-2 is the virus responsible for the recent global health crisis, as it causes the coronavirus disease-19 (COVID-19) in humans. Due to its high rate of spreading and significant fatality rates, the situation has escalated to a pandemic, which is the cause of immense disruption in daily life. In this study, we have taken a docking-based virtual screening approach to select natural molecules (from plants) with possible therapeutic potential. For this purpose, AUTODOCK Vina-based determination of binding affinity values (blind and active-site oriented) was obtained to short-list molecules with possible inhibitory potential against the main Mpro in SARS-CoV-2 (PDB ID 6Y2F -the monomeric form). The 4 molecules selected were Chebuloside (−8.2; −8.2), Acetoside (−8.0; −8.0), Corilagin (−8.1; −7.7) and Arjunolic Acid (−8.0; −7.6) (blind and active-site oriented docking scores (Kcal/mol) in parenthesis, respectively). Further, a comparative search, with FDA-approved drugs, has shown that Ouabain was comparable to Chebuloside with a similarity score of 0.227. This in silico finding with respect to Ouabain is significant, since this polycyclic glycoside has been shown to treat COVID-19 positive patients with a cardiovascular disease. Hydrocortisone was similar to Arjunolic acid with a score of 0.539. Again, this likeness is worthy of mention, since hydrocortisone has been used earlier for the treatment of SARS-CoV1 and MERS. However, further experimentation and validation of the results, in suitable biological model systems, are necessary to gain more insight and relevance as well as provide corroborative evidence for our in-silico findings.
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Chan CTM, Leung JSL, Lee LK, Lo HWH, Wong EYK, Wong DSH, Ng TTL, Lao HY, Lu KK, Jim SHC, Yau MCY, Lam JYW, Ho AYM, Luk KS, Yip KT, Que TL, To KKW, Siu GKH. A low-cost TaqMan minor groove binder probe-based one-step RT-qPCR assay for rapid identification of N501Y variants of SARS-CoV-2. J Virol Methods 2022; 299:114333. [PMID: 34656702 PMCID: PMC8516123 DOI: 10.1016/j.jviromet.2021.114333] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/01/2021] [Accepted: 10/11/2021] [Indexed: 12/17/2022]
Abstract
The increasing prevalence of N501Y variants of SARS-CoV-2 has kindled global concern due to their enhanced transmissibility. Genome sequencing is the gold standard method to identify the emerging variants of concern. But it is time-consuming and expensive, limiting the widespread deployment of genome surveillance in some countries. Health authorities surge the development of alternative assay to expand screening capacity with reduced time and cost. In this study, we developed an in-house TaqMan minor groove binder (MGB) probe-based one-step RT-qPCR assay to detect the presence of N501Y mutation in SARS-CoV-2. A total of 168 SARS-CoV-2 positive respiratory specimens were collected to determine diagnostic accuracy of the RT-qPCR assay. As a reference standard, PANGO lineages and the mutation patterns of all samples were characterised by whole-genome sequencing. The analytical sensitivity and the ability of the assay to detect low frequency of N501Y variants were also evaluated. A total of 31 PANGO lineages were identified from 168 SARS-CoV-2 positive cases, in which 34 samples belonged to N501Y variants, including B.1.1.7 (n = 20), B.1.351 (n = 12) and P.3 (n = 2). The N501Y RT-qPCR correctly identified all 34 samples as N501Y-positive and the other 134 samples as wildtype. The limit-of-detection of the assay consistently achieved 1.5 copies/μL on four different qPCR platforms. N501Y mutation was successfully detected at an allele frequency as low as 10 % in a sample with mixed SARS-CoV-2 lineage. The N501Y RT-qPCR is simple and inexpensive (US$1.6 per sample). It enables robust high-throughput screening for surveillance of SARS-CoV-2 variants of concern harbouring N501Y mutation.
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Affiliation(s)
- Chloe Toi-Mei Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Jake Siu-Lun Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Lam-Kwong Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Hazel Wing-Hei Lo
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Evelyn Yin-Kwan Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Denise Sze-Hang Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Timothy Ting-Leung Ng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Hiu-Yin Lao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Kelvin Keru Lu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Stephanie Hoi-Ching Jim
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Miranda Chong-Yee Yau
- Department of Clinical Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong Special Administrative Region
| | - Jimmy Yiu-Wing Lam
- Department of Clinical Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong Special Administrative Region
| | - Alex Yat-Man Ho
- Department of Pathology, Princess Margaret Hospital, Hong Kong Special Administrative Region
| | - Kristine Shik Luk
- Department of Pathology, Princess Margaret Hospital, Hong Kong Special Administrative Region
| | - Kam-Tong Yip
- Department of Clinical Pathology, Tuen Mun Hospital, Hong Kong Special Administrative Region
| | - Tak-Lun Que
- Department of Clinical Pathology, Tuen Mun Hospital, Hong Kong Special Administrative Region
| | - Kelvin Kai-Wang To
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.
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Cheng VCC, Siu GKH, Wong SC, Au AKW, Ng CSF, Chen H, Li X, Lee LK, Leung JSL, Lu KK, Lo HWH, Wong EYK, Luk S, Lam BHS, To WK, Lee RA, Lung DC, Kwan MYW, Tse H, Chuang SK, To KKW, Yuen KY. Complementation of contact tracing by mass testing for successful containment of beta COVID-19 variant (SARS-CoV-2 VOC B.1.351) epidemic in Hong Kong. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2021; 17:100281. [PMID: 34611629 PMCID: PMC8483778 DOI: 10.1016/j.lanwpc.2021.100281] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND Global dissemination of SARS-CoV-2 Variants of Concern (VOCs) remains a concern. The aim of this study is to describe how mass testing and phylogenetic analysis successfully prevented local transmission of SARS-CoV-2 VOC in a densely populated city with low herd immunity for COVID-19. METHODS In this descriptive study, we conducted contact tracing, quarantine, and mass testing of the potentially exposed contacts with the index case. Epidemiological investigation and phylogeographic analysis were performed. FINDINGS Among 11,818 laboratory confirmed cases of COVID-19 diagnosed till 13th May 2021 in Hong Kong, SARS-CoV-2 VOCs were found in 271 (2.3%) cases. Except for 10 locally acquired secondary cases, all SARS-CoV-2 VOCs were imported or acquired in quarantine hotels. The index case of this SARS-CoV-2 VOC B.1.351 epidemic, an inbound traveler with asymptomatic infection, was diagnosed 9 days after completing 21 days of quarantine. Contact tracing of 163 contacts in household, hotel, and residential building only revealed 1 (0.6%) secondary case. A symptomatic foreign domestic helper (FDH) without apparent epidemiological link but infected by virus with identical genome sequence was subsequently confirmed. Mass testing of 0.34 million FDHs identified two more cases which were phylogenetically linked. A total of 10 secondary cases were identified that were related to two household gatherings. The clinical attack rate of household close contact was significantly higher than non-household exposure during quarantine (7/25, 28% vs 0/2051, 0%; p<0.001). INTERPRETATION The rising epidemic of SARS-CoV-2 VOC transmission could be successfully controlled by contact tracing, quarantine, and rapid genome sequencing complemented by mass testing. FUNDING Health and Medical Research Fund Commissioned Research on Control of Infectious Disease (see acknowledgments for full list).
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Affiliation(s)
- Vincent Chi-Chung Cheng
- Infection Control Team, Queen Mary Hospital, Hong Kong West Cluster, Hong Kong Special Administrative Region, China
- Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Shuk-Ching Wong
- Infection Control Team, Queen Mary Hospital, Hong Kong West Cluster, Hong Kong Special Administrative Region, China
| | - Albert Ka-Wing Au
- Centre for Health Protection, Department of Health, Hong Kong Special Administrative Region, China
| | - Cecilia Suk-Fun Ng
- Centre for Health Protection, Department of Health, Hong Kong Special Administrative Region, China
| | - Hong Chen
- Centre for Health Protection, Department of Health, Hong Kong Special Administrative Region, China
| | - Xin Li
- Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Lam-Kwong Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Jake Siu-Lun Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Kelvin Keru Lu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Hazel Wing-Hei Lo
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Evelyn Yin-Kwan Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Shik Luk
- Department of Pathology, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Bosco Hoi-Shiu Lam
- Department of Pathology, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Wing-Kin To
- Department of Pathology, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Rodney Allan Lee
- Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong Special Administrative Region, China
| | - David Christopher Lung
- Department of Pathology, Hong Kong Children's Hospital / Queen Elizabeth Hospital, Hong Kong Special Administrative Region, China
| | - Mike Yat-Wah Kwan
- Department of Paediatrics and Adolescent Medicine, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Herman Tse
- Department of Pathology, Hong Kong Children's Hospital, Hong Kong Special Administrative Region, China
| | - Shuk-Kwan Chuang
- Centre for Health Protection, Department of Health, Hong Kong Special Administrative Region, China
| | - Kelvin Kai-Wang To
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kwok-Yung Yuen
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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9
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Quantifying superspreading for COVID-19 using Poisson mixture distributions. Sci Rep 2021; 11:14107. [PMID: 34238978 PMCID: PMC8266910 DOI: 10.1038/s41598-021-93578-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 06/22/2021] [Indexed: 12/23/2022] Open
Abstract
The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, [Formula: see text], while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.
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10
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Chu DKW, Hui KPY, Gu H, Ko RLW, Krishnan P, Ng DYM, Liu GYZ, Wan CKC, Cheung MC, Ng KC, Nicholls JM, Tsang DNC, Peiris M, Chan MCW, Poon LLM. Introduction of ORF3a-Q57H SARS-CoV-2 Variant Causing Fourth Epidemic Wave of COVID-19, Hong Kong, China. Emerg Infect Dis 2021; 27:1492-1495. [PMID: 33900193 PMCID: PMC8084491 DOI: 10.3201/eid2705.210015] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
We describe an introduction of clade GH severe acute respiratory syndrome coronavirus 2 causing a fourth wave of coronavirus disease in Hong Kong. The virus has an ORF3a-Q57H mutation, causing truncation of ORF3b. This virus evades induction of cytokine, chemokine, and interferon-stimulated gene expression in primary human respiratory cells.
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11
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Mak GCK, Lau AWL, Chan AMY, Lam ETK, Chan RCW, Tsang DNC. The surveillance of spike protein for patients with COVID-19 detected in Hong Kong in 2020. J Med Virol 2021; 93:5644-5647. [PMID: 33951208 PMCID: PMC8242547 DOI: 10.1002/jmv.27063] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/23/2021] [Accepted: 05/03/2021] [Indexed: 01/17/2023]
Abstract
In 2020, numerous fast-spreading severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been reported. These variants had unusually high genetic changes in the spike (S) protein. In an attempt to understand the genetic background of SARS-CoV-2 viruses in Hong Kong, especially before vaccination, the purpose of this study is to summarize the S protein mutations detected among coronavirus disease 2019 (COVID-19) patients in Hong Kong in 2020. COVID-19 cases were selected every month in 2020. One virus from each case was analyzed. The full encoding region of the S proteins was sequenced. From January 2020 to December 2020, a total of 340 COVID-19 viruses were sequenced. The amino acids of the S protein for 44 (12.9%) were identical to the reference sequence, WIV04 (GenBank accession MN996528). For the remaining 296 sequences (87.1%), a total of 43 nonsynonymous substitution patterns were found. Of the nonsynonymous substitutions found, some of them were only detected at specific time intervals and then they disappeared. The ongoing genetic surveillance system is important. It would facilitate early detection of mutations that can increase infectivity as well as mutations that are selected for the virus to escape immunological restraint.
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Affiliation(s)
- Gannon C K Mak
- All from Microbiology Division, Department of Health, Public Health Laboratory Services Branch, Centre for Health Protection, Hong Kong Special Administrative Region, China
| | - Angela W L Lau
- All from Microbiology Division, Department of Health, Public Health Laboratory Services Branch, Centre for Health Protection, Hong Kong Special Administrative Region, China
| | - Andy M Y Chan
- All from Microbiology Division, Department of Health, Public Health Laboratory Services Branch, Centre for Health Protection, Hong Kong Special Administrative Region, China
| | - Edman T K Lam
- All from Microbiology Division, Department of Health, Public Health Laboratory Services Branch, Centre for Health Protection, Hong Kong Special Administrative Region, China
| | - Rickjason C W Chan
- All from Microbiology Division, Department of Health, Public Health Laboratory Services Branch, Centre for Health Protection, Hong Kong Special Administrative Region, China
| | - Dominic N C Tsang
- All from Microbiology Division, Department of Health, Public Health Laboratory Services Branch, Centre for Health Protection, Hong Kong Special Administrative Region, China
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12
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Clemente-Suárez VJ, Navarro-Jiménez E, Ruisoto P, Dalamitros AA, Beltran-Velasco AI, Hormeño-Holgado A, Laborde-Cárdenas CC, Tornero-Aguilera JF. Performance of Fuzzy Multi-Criteria Decision Analysis of Emergency System in COVID-19 Pandemic. An Extensive Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18105208. [PMID: 34068866 PMCID: PMC8153618 DOI: 10.3390/ijerph18105208] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/10/2021] [Accepted: 05/12/2021] [Indexed: 12/20/2022]
Abstract
The actual coronavirus disease 2019 (COVID-19) pandemic has led to the limit of emergency systems worldwide, leading to the collapse of health systems, police, first responders, as well as other areas. Various ways of dealing with this world crisis have been proposed from many aspects, with fuzzy multi-criteria decision analysis being a method that can be applied to a wide range of emergency systems and professional groups, aiming to confront several associated issues and challenges. The purpose of this critical review was to discuss the basic principles, present current applications during the first pandemic wave, and propose future implications of this methodology. For this purpose, both primary sources, such as scientific articles, and secondary ones, such as bibliographic indexes, web pages, and databases, were used. The main search engines were PubMed, SciELO, and Google Scholar. The method was a systematic literature review of the available literature regarding the performance of the fuzzy multi-criteria decision analysis of emergency systems in the COVID-19 pandemic. The results of this study highlight the importance of the fuzzy multi-criteria decision analysis method as a beneficial tool for healthcare workers and first responders’ emergency professionals to face this pandemic as well as to manage the created uncertainty and its related risks.
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Affiliation(s)
- Vicente Javier Clemente-Suárez
- Faculty of Sports Sciences, Universidad Europea de Madrid, Tajo Street, s/n, 28670 Madrid, Spain;
- Grupo de Investigación en Cultura, Educación y Sociedad, Universidad de la Costa, Barranquilla 080002, Colombia
- Studies Centre in Applied Combat (CESCA), 45007 Toledo, Spain;
- Correspondence: ; Fax: +34-911-413-585
| | - Eduardo Navarro-Jiménez
- Grupo de investigacion en Microbiologia y Biotecnologia (IMB), Universidad Libre, Barranquilla 08002, Colombia;
| | - Pablo Ruisoto
- Department of Health Sciences, Public University of Navarre, 31006 Pamplona, Spain;
| | - Athanasios A. Dalamitros
- Laboratory of Evaluation of Human Biological Performance, School of Physical Education and Sport Sciences, Aristotle University of Thessaloniki, 57001 Thessaloniki, Greece;
| | | | | | | | - Jose Francisco Tornero-Aguilera
- Faculty of Sports Sciences, Universidad Europea de Madrid, Tajo Street, s/n, 28670 Madrid, Spain;
- Studies Centre in Applied Combat (CESCA), 45007 Toledo, Spain;
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13
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Ghram A, Bragazzi NL, Briki W, Jenab Y, Khaled M, Haddad M, Chamari K. COVID-19 Pandemic and Physical Exercise: Lessons Learnt for Confined Communities. Front Psychol 2021; 12:618585. [PMID: 34025498 PMCID: PMC8131539 DOI: 10.3389/fpsyg.2021.618585] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 04/08/2021] [Indexed: 12/18/2022] Open
Abstract
The novel pandemic called "Coronavirus Disease 2019" (COVID-19), as a global public health emergency and global threat, has affected many countries in unpredictable ways and impacted on physical activity (PA) behaviors to various extents. Specific populations including refugees, asylum seekers, and prisoners, are vulnerable groups with multiple complex health needs and worse health outcomes with respect to the general population worldwide and at high risk of death from the "Severe Acute Respiratory Syndrome-related Coronavirus type 2" (SARS-CoV-2). Governments around the world have been implementing preventive healthcare policies, including physical and social distancing, isolation, and confinement, to mitigate against the burden imposed by the COVID-19 outbreak. This pandemic period is characterized by reduced or lack of movement. During this period of lockdown, PA can represent an immunotherapy and a preventative approach to avoid the harmful effects of inactivity due to the pandemic. Moreover, PA could be prescribed to improve the immune system of specific populations (refugees, asylum seekers, and prisoners), which particularly experience the condition of being confined. The present narrative review discusses the potential impacts of COVID-19 pandemic on these specific populations' health status and the importance of performing PA/exercise to reduce the deleterious effects of COVID-19 pandemic. In addition, we aim to provide useful recommendations on PA/exercise for these specific populations to maintain their level of independence, physical, and mental health as well as their wellbeing.
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Affiliation(s)
- Amine Ghram
- Department of Exercise Physiology, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran
- Department of Cardiac Rehabilitation, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Walid Briki
- Department of Physical Education, College of Education, Qatar University, Doha, Qatar
| | - Yaser Jenab
- Department of Interventional Cardiology, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Khaled
- Independent Physician (Internal Medicine), Singapore, Singapore
| | - Monoem Haddad
- Department of Physical Education, College of Education, Qatar University, Doha, Qatar
| | - Karim Chamari
- Aspetar, Qatar Orthopaedic and Sports Medicine Hospital, Doha, Qatar
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14
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Chan WM, Ip JD, Chu AWH, Tse H, Tam AR, Li X, Kwan MYW, Yau YS, Leung WS, Chik TSH, To WK, Ng ACK, Yip CCY, Poon RWS, Chan KH, Wong SCY, Choi GKY, Lung DC, Cheng VCC, Hung IFN, Yuen KY, To KKW. Phylogenomic analysis of COVID-19 summer and winter outbreaks in Hong Kong: An observational study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2021; 10:100130. [PMID: 33778795 PMCID: PMC7985010 DOI: 10.1016/j.lanwpc.2021.100130] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/01/2021] [Accepted: 03/02/2021] [Indexed: 01/04/2023]
Abstract
BACKGROUND Viral genomic surveillance is vital for understanding the transmission of COVID-19. In Hong Kong, breakthrough outbreaks have occurred in July (third wave) and November (fourth wave) 2020. We used whole viral genome analysis to study the characteristics of these waves. METHODS We analyzed 509 SARS-CoV-2 genomes collected from Hong Kong patients between 22nd January and 29th November, 2020. Phylogenetic and phylodynamic analyses were performed, and were interpreted with epidemiological information. FINDINGS During the third and fourth waves, diverse SARS-CoV-2 genomes were identified among imported infections. Conversely, local infections were dominated by a single lineage during each wave, with 96.6% (259/268) in the third wave and 100% (73/73) in the fourth wave belonging to B.1.1.63 and B.1.36.27 lineages, respectively. While B.1.1.63 lineage was imported 2 weeks before the beginning of the third wave, B.1.36.27 lineage has circulated in Hong Kong for 2 months prior to the fourth wave. During the fourth wave, 50.7% (37/73) of local infections in November was identical to the viral genome from an imported case in September. Within B.1.1.63 or B.1.36.27 lineage in our cohort, the most common non-synonymous mutations occurred at the helicase (nsp13) gene. INTERPRETATION Although stringent measures have prevented most imported cases from spreading in Hong Kong, a single lineage with low-level local transmission in October and early November was responsible for the fourth wave. A superspreading event or lower temperature in November may have facilitated the spread of the B.1.36.27 lineage.
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Affiliation(s)
- Wan-Mui Chan
- State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - Jonathan Daniel Ip
- State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - Allen Wing-Ho Chu
- State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - Herman Tse
- Department of Pathology, Hong Kong Children's Hospital, Kowloon, Hong Kong Special Administrative Region, China
| | - Anthony Raymond Tam
- Department of Medicine, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Xin Li
- State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
- Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Mike Yat-Wah Kwan
- Department of Paediatrics and Adolescent Medicine, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Yat-Sun Yau
- Department of Paediatrics, Queen Elizabeth Hospital, Kowloon, Hong Kong Special Administrative Region, China
| | - Wai-Shing Leung
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Thomas Shiu-Hong Chik
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Wing-Kin To
- Department of Pathology, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Anthony Chin-Ki Ng
- State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - Cyril Chik-Yan Yip
- Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Rosana Wing-Shan Poon
- Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Kwok-Hung Chan
- State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - Sally Cheuk-Ying Wong
- Department of Pathology, Hong Kong Children's Hospital, Kowloon, Hong Kong Special Administrative Region, China
| | - Garnet Kwan-Yue Choi
- Department of Pathology, Hong Kong Children's Hospital, Kowloon, Hong Kong Special Administrative Region, China
- Department of Pathology, Queen Elizabeth Hospital, Kowloon, Hong Kong Special Administrative Region, China
| | - David Christopher Lung
- Department of Pathology, Hong Kong Children's Hospital, Kowloon, Hong Kong Special Administrative Region, China
- Department of Pathology, Queen Elizabeth Hospital, Kowloon, Hong Kong Special Administrative Region, China
| | - Vincent Chi-Chung Cheng
- State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
- Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Ivan Fan-Ngai Hung
- Department of Medicine, Queen Mary Hospital, Hong Kong Special Administrative Region, China
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - Kwok-Yung Yuen
- State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
- Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Kelvin Kai-Wang To
- State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
- Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
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15
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Cheng VCC, Fung KSC, Siu GKH, Wong SC, Cheng LSK, Wong MS, Lee LK, Chan WM, Chau KY, Leung JSL, Chu AWH, Chan WS, Lu KK, Tam KKG, Ip JD, Leung KSS, Lung DC, Tse H, To KKW, Yuen KY. Nosocomial outbreak of COVID-19 by possible airborne transmission leading to a superspreading event. Clin Infect Dis 2021; 73:e1356-e1364. [PMID: 33851214 PMCID: PMC8083289 DOI: 10.1093/cid/ciab313] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Indexed: 12/24/2022] Open
Abstract
Background Nosocomial outbreaks with superspreading of COVID-19 due to a possible airborne transmission has not been reported. Methods Epidemiological analysis, environmental samplings, and whole genome sequencing (WGS) were performed for a hospital outbreak. Results A superspreading event involving 12 patients and 9 healthcare workers (HCWs) occurred within 4 days in 3 of 6 cubicles at an old-fashioned general ward with no air exhaust built within the cubicles. The environmental contamination by SARS-CoV-2 RNA was significantly higher in air grilles (>2m from patients’ head and not reachable by hands) than high-touch clinical surfaces (36.4%, 8/22 vs 3.4%, 1/29, p=0.003). Six (66.7%) of 9 contaminated air exhaust grilles were located outside patient cubicle. The clinical attack rate of patients was significantly higher than HCWs (15.4%, 12/78 exposed-patients vs 4.6%, 9/195 exposed-HCWs, p=0.005). Moreover, clinical attack rate of ward-based HCWs was significantly higher than non-ward-based HCWs (8.1%, 7/68 vs 1.8%, 2/109, p=0.045). The episodes (mean ± S.D) of patient-care duty assignment in the cubicles was significantly higher among infected ward-based HCWs than non-infected ward-based HCWs (6.0±2.4 vs 3.0±2.9, p=0.012) during the outbreak period. The outbreak strains belong to SARS-CoV-2 lineage, B.1.36.27 (GISAID Clade GH) with the unique S-T470N mutation on WGS. Conclusion This nosocomial point source superspreading due to possible airborne transmission demonstrated the need for stringent SARS-CoV-2 screening at admission to healthcare facilities and better architectural design of the ventilation system to prevent such outbreaks. Portable high-efficiency particulate filters were installed in each cubicle to improve ventilation before resumption of clinical service.
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Affiliation(s)
- Vincent Chi-Chung Cheng
- Infection Control Team, Queen Mary Hospital, Hong Kong West Cluster, Hong Kong Special Administrative Region, China.,Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Kitty Sau-Chun Fung
- Department of Pathology and Infection Control Team, United Christian Hospital, Hong Kong Special Administrative Region, China
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Shuk-Ching Wong
- Infection Control Team, Queen Mary Hospital, Hong Kong West Cluster, Hong Kong Special Administrative Region, China
| | - Lily Shui-Kuen Cheng
- Department of Pathology and Infection Control Team, United Christian Hospital, Hong Kong Special Administrative Region, China
| | - Man-Sing Wong
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Lam-Kwong Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Wan-Mui Chan
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ka-Yee Chau
- Department of Pathology and Infection Control Team, United Christian Hospital, Hong Kong Special Administrative Region, China
| | - Jake Siu-Lun Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Allen Wing-Ho Chu
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Wai-Shan Chan
- Department of Pathology and Infection Control Team, United Christian Hospital, Hong Kong Special Administrative Region, China
| | - Kelvin Keru Lu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Kingsley King-Gee Tam
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jonathan Daniel Ip
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kenneth Siu-Sing Leung
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - David Christopher Lung
- Department of Pathology, Hong Kong Children's Hospital / Queen Elizabeth Hospital, Hong Kong Special Administrative Region, China
| | - Herman Tse
- Department of Pathology, Hong Kong Children's Hospital, Hong Kong Special Administrative Region, China
| | - Kelvin Kai-Wang To
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kwok-Yung Yuen
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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