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Guo F, Zhang P, Do V, Runge J, Zhang K, Han Z, Deng S, Lin H, Ali ST, Chen R, Guo Y, Tian L. Ozone as an environmental driver of influenza. Nat Commun 2024; 15:3763. [PMID: 38704386 PMCID: PMC11069565 DOI: 10.1038/s41467-024-48199-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 04/23/2024] [Indexed: 05/06/2024] Open
Abstract
Under long-standing threat of seasonal influenza outbreaks, it remains imperative to understand the drivers of influenza dynamics which can guide mitigation measures. While the role of absolute humidity and temperature is extensively studied, the possibility of ambient ozone (O3) as an environmental driver of influenza has received scant attention. Here, using state-level data in the USA during 2010-2015, we examined such research hypothesis. For rigorous causal inference by evidence triangulation, we applied 3 distinct methods for data analysis: Convergent Cross Mapping from state-space reconstruction theory, Peter-Clark-momentary-conditional-independence plus as graphical modeling algorithms, and regression-based Generalised Linear Model. The negative impact of ambient O3 on influenza activity at 1-week lag is consistently demonstrated by those 3 methods. With O3 commonly known as air pollutant, the novel findings here on the inhibition effect of O3 on influenza activity warrant further investigations to inform environmental management and public health protection.
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Affiliation(s)
- Fang Guo
- School of Public Health, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China
| | - Pei Zhang
- School of Public Health, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China
| | - Vivian Do
- Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Jakob Runge
- Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Datenwissenschaften, Jena, Germany
- Technische Universität Berlin, Berlin, Germany
| | - Kun Zhang
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
| | - Zheshen Han
- School of Public Health, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China
| | - Shenxi Deng
- School of Public Health, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China
| | - Hongli Lin
- School of Public Health, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China
| | - Sheikh Taslim Ali
- School of Public Health, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong SAR, PR China
| | - Ruchong Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, Department of Allergy and Clinical Immunology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, PR China
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Linwei Tian
- School of Public Health, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China.
- Institute for Climate and Carbon Neutrality, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China.
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2
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Ali ST, Wu P, He D, Tian L, Cowling BJ. Forecasting influenza epidemics in Hong Kong using multiple streams of syndromic and laboratory surveillance data: abridged secondary publication. Hong Kong Med J 2024; 30 Suppl 1:4-8. [PMID: 38413204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024] Open
Affiliation(s)
- S T Ali
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - P Wu
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - D He
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - L Tian
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - B J Cowling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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3
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Ryu S, Han C, Ali ST, Achangwa C, Yang B, Pei S. Corrigendum to "Association of public health and social measures on the hand-foot-mouth epidemic in South Korea" [J Infect Public Health 16 (2023) 859-64]. J Infect Public Health 2023; 16:1891. [PMID: 37558591 DOI: 10.1016/j.jiph.2023.07.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023] Open
Affiliation(s)
- Sukhyun Ryu
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea.
| | - Changhee Han
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea; Business Analytics, University of Texas at Dallas, Dallas, USA
| | - Sheikh Taslim Ali
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China; Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, New Territories, Hong Kong, China
| | - Chiara Achangwa
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea
| | - Bingyi Yang
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, USA
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4
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Bai Y, Shao Z, Zhang X, Chen R, Wang L, Ali ST, Chen T, Lau EHY, Jin DY, Du Z. Reproduction number of SARS-CoV-2 Omicron variants, China, December 2022-January 2023. J Travel Med 2023; 30:taad049. [PMID: 37043284 DOI: 10.1093/jtm/taad049] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 04/04/2023] [Indexed: 04/13/2023]
Abstract
China adjusted the zero-COVID strategy in late 2022, triggering an unprecedented Omicron wave. We estimated the time-varying reproduction numbers of 32 provincial-level administrative divisions from December 2022 to January 2023. We found that the pooled estimate of initial reproduction numbers is 4.74 (95% confidence interval: 4.41, 5.07).
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Affiliation(s)
- Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Hong Kong Special Admimistrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Zengyang Shao
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Xiao Zhang
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Ruohan Chen
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Hong Kong Special Admimistrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Hong Kong Special Admimistrative Region, China
| | - Dong-Yan Jin
- Li Ka Shing Faculty of Medicine, School of Biomedical Sciences, The University of Hong Kong, Hong Kong Special Admimistrative Region, China
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Hong Kong Special Admimistrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
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Ryu S, Han C, Ali ST, Achangwa C, Yang B, Pei S. Association of public health and social measures on the hand-foot-mouth epidemic in South Korea. J Infect Public Health 2023; 16:859-864. [PMID: 37031625 DOI: 10.1016/j.jiph.2023.03.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/17/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND School based-measures such as school closure and school holidays have been considered a viable intervention during the hand-foot-mouth disease (HFMD) epidemic. The aim of this study was to explore the association of nationwide public health and social measures (PHSMs) including planned school vacation on the transmissibility and attack rate of the HFMD epidemic in South Korea. METHODS In this study, we used Korean national surveillance data on HFMD from 2014 to 2019 to estimate the temporal changes in HFMD transmissibility (instantaneous reproductive number, Rt). Furthermore, to assess the changes in the HFMD attack rate, we used a stochastic transmission model to simulate the HFMD epidemic with no school vacation and nationwide PHSMs in 2015 South Korea. RESULTS We found that school vacations and 2015 PHSMs were associated with the reduced Rt by 2-7 % and 13 %, respectively. Model projections indicated school vacations and 2015 PHSMs were associated with reduced HFMD attack rate by an average of 1.10 % (range: 0.38-1.51 %). CONCLUSIONS PHSMs likely have a larger association with reduced HFMD transmissibility than school-based measures alone (i.e. school vacations). Preventive measures targeting preschoolers could be considered as potential options for reducing the future burden of HFMD.
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Affiliation(s)
- Sukhyun Ryu
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea.
| | - Changhee Han
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea; Business Analytics, University of Texas at Dallas, Dallas, USA
| | - Sheikh Taslim Ali
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China; Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, New Territories, Hong Kong, China
| | - Chiara Achangwa
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea
| | - Bingyi Yang
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, USA
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He D, Cowling BJ, Ali ST, Stone L. Rapid global spread of variants of concern of SARS-CoV-2. IJID Reg 2023; 7:63-65. [PMID: 36569559 PMCID: PMC9763203 DOI: 10.1016/j.ijregi.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/27/2022]
Abstract
Objectives Variants of concern (VOCs) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), such as the Delta variant and the Omicron variant, have reached all countries/regions of the world and have had a tremendous impact. This study analyses the global spread of VOCs of SARS-CoV-2. Methods Biweekly aggregated numbers of several VOCs were retrieved for 58 locations. The time interval for the proportion of VOC samples to exceed 60% (indicating dominance) among all samples sequenced in each location was calculated. The times taken for a VOC to become dominant in 12 (or 36) locations was defined in order to quantify the speed of spread. Results It took 63, 56 and 28 days for the Alpha, Delta and Omicron variants to become dominant in 12 locations, respectively, and 133, 70 and 28 days for the Alpha, Delta and Omicron variants to become dominant in 36 locations. Conclusions The Omicron variant has much higher transmission potential compared with the Delta variant, and the Delta variant has higher transmission potential compared with the pre-Delta VOCs.
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Affiliation(s)
- Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China
- Research Institute for Future Food, Hong Kong Polytechnic University, Hong Kong, China
| | - Benjamin J Cowling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Sheikh Taslim Ali
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Lewi Stone
- Mathematical Sciences, School of Science, RMIT University, Melbourne, Australia
- Biomathematics Unit, School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
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7
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Shao Z, Ma L, Bai Y, Tan Q, Liu XF, Liu S, Ali ST, Wang L, Lau EHY, Cowling BJ, Du Z. Impact of mass rapid antigen testing for SARS-CoV-2 to mitigate Omicron outbreaks in China. J Travel Med 2022; 29:6713539. [PMID: 36263876 PMCID: PMC9619432 DOI: 10.1093/jtm/taac110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 11/05/2022]
Abstract
We analysed the effectiveness of various non-pharmaceutical interventions in containing the 2022 Omicron outbreak in China. The results show that the Rapid Antigen Test contributed to containing the outbreak, reducing the reproduction number by 0.788 (95% CI:−0.306, 1.880) in studied cities.
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Affiliation(s)
- Zengyang Shao
- College of Computer Science and Software Engineering, ShenZhen University, Shen Zhen, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong, SAR, China
| | - Lijia Ma
- College of Computer Science and Software Engineering, ShenZhen University, Shen Zhen, China
| | - Yuan Bai
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong, SAR, China.,WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Qi Tan
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Xiao Fan Liu
- Department of Media and Communication, City University of Hong Kong, Hong Kong, SAR, China
| | - Shiyong Liu
- Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University at Zhuhai, Zhuhai, China
| | - Sheikh Taslim Ali
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong, SAR, China.,WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Eric H Y Lau
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong, SAR, China.,WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Benjamin J Cowling
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong, SAR, China.,WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Zhanwei Du
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong, SAR, China.,WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
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Ali ST, Lau YC, Shan S, Ryu S, Du Z, Wang L, Xu XK, Chen D, Xiong J, Tae J, Tsang TK, Wu P, Lau EHY, Cowling BJ. Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study. Lancet Glob Health 2022; 10:e1612-e1622. [PMID: 36240828 PMCID: PMC9573849 DOI: 10.1016/s2214-109x(22)00358-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming influenza epidemics. METHODS For this modelling study, we used surveillance data on influenza virus activity for 11 different locations and countries in 2017-22. We implemented a data-driven mechanistic predictive modelling framework to predict future influenza seasons on the basis of pre-COVID-19 dynamics and the effect of PHSMs during the COVID-19 pandemic. We simulated the potential excess burden of upcoming influenza epidemics in terms of fold rise in peak magnitude and epidemic size compared with pre-COVID-19 levels. We also examined how a proactive influenza vaccination programme could mitigate this effect. FINDINGS We estimated that COVID-19 PHSMs reduced influenza transmissibility by a maximum of 17·3% (95% CI 13·3-21·4) to 40·6% (35·2-45·9) and attack rate by 5·1% (1·5-7·2) to 24·8% (20·8-27·5) in the 2019-20 influenza season. We estimated a 10-60% increase in the population susceptibility for influenza, which might lead to a maximum of 1-5-fold rise in peak magnitude and 1-4-fold rise in epidemic size for the upcoming 2022-23 influenza season across locations, with a significantly higher fold rise in Singapore and Taiwan. The infection burden could be mitigated by additional proactive one-off influenza vaccination programmes. INTERPRETATION Our results suggest the potential for substantial increases in infection burden in upcoming influenza seasons across the globe. Strengthening influenza vaccination programmes is the best preventive measure to reduce the effect of influenza virus infections in the community. FUNDING Health and Medical Research Fund, Hong Kong.
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Affiliation(s)
- Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Yiu Chung Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Songwei Shan
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Sukhyun Ryu
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Xiao-Ke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Dalian, China
| | - Dongxuan Chen
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Jiaming Xiong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Jungyeon Tae
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China,Correspondence to: Prof Benjamin J Cowling, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
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9
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Abstract
To model estimated deaths averted by COVID-19 vaccines, we used state-of-the-art mathematical modeling, likelihood-based inference, and reported COVID-19 death and vaccination data. We estimated that >1.5 million deaths were averted in 12 countries. Our model can help assess effectiveness of the vaccination program, which is crucial for curbing the COVID-19 pandemic.
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Mefsin YM, Chen D, Bond HS, Lin Y, Cheung JK, Wong JY, Ali ST, Lau EHY, Wu P, Leung GM, Cowling BJ. Epidemiology of Infections with SARS-CoV-2 Omicron BA.2 Variant, Hong Kong, January-March 2022. Emerg Infect Dis 2022; 28:1856-1858. [PMID: 35914518 PMCID: PMC9423929 DOI: 10.3201/eid2809.220613] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Our analysis of data collected from multiple epidemics in Hong Kong indicated a shorter serial interval and generation time of infections with the SARS-CoV-2 Omicron variant. The age-specific case-fatality risk for Omicron BA.2.2 case-patients without complete primary vaccination was comparable to that of persons infected with ancestral strains in earlier waves.
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11
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Ali ST, Cowling BJ, Wong JY, Chen D, Shan S, Lau EHY, He D, Tian L, Li Z, Wu P. Influenza seasonality and its environmental driving factors in mainland China and Hong Kong. Sci Total Environ 2022; 818:151724. [PMID: 34800462 DOI: 10.1016/j.scitotenv.2021.151724] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/20/2021] [Accepted: 11/12/2021] [Indexed: 05/27/2023]
Abstract
BACKGROUND Influenza epidemics occur during winter in temperate zones, but have less regular seasonality in the subtropics and tropics. Here we quantified the role of environmental drivers of influenza seasonality in temperate and subtropical China. METHODS We used weekly surveillance data on influenza virus activity in mainland China and Hong Kong from 2005 through 2016. We estimated the transmissibility via the instantaneous reproduction number (Rt), a real-time measure of transmissibility, and examined its relationship with different climactic drivers and allowed for the timing of school holidays and the decline in susceptibility in the population as an epidemic progressed. We developed a multivariable regression model for Rt to quantify the contribution of various potential environmental drivers of transmission. FINDINGS We found that absolute humidity is a potential driver of influenza seasonality and had a U-shaped association with transmissibility and hence can predict the pattern of influenza virus transmission across different climate zones. Absolute humidity was able to explain up to 15% of the variance in Rt, and was a stronger predictor of Rt across the latitudes. Other climatic drivers including mean daily temperature explained up to 13% of variance in Rt and limited to the locations where the indoor measures of these factors have better indicators of outdoor measures. The non-climatic driver, holiday-related school closures could explain up to 7% of variance in Rt. INTERPRETATION A U-shaped association of absolute humidity with influenza transmissibility was able to predict seasonal patterns of influenza virus epidemics in temperate and subtropical locations.
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Affiliation(s)
- Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region.
| | - Jessica Y Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Dongxuan Chen
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region
| | - Songwei Shan
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Linwei Tian
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Zhongjie Li
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region
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12
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Kang M, Xin H, Yuan J, Ali ST, Liang Z, Zhang J, Hu T, Lau EH, Zhang Y, Zhang M, Cowling BJ, Li Y, Wu P. Transmission dynamics and epidemiological characteristics of SARS-CoV-2 Delta variant infections in Guangdong, China, May to June 2021. Euro Surveill 2022; 27. [PMID: 35272744 PMCID: PMC8915401 DOI: 10.2807/1560-7917.es.2022.27.10.2100815] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background The Delta variant of SARS-CoV-2 had become predominant globally by November 2021. Aim We evaluated transmission dynamics and epidemiological characteristics of the Delta variant in an outbreak in southern China. Methods Data on confirmed COVID-19 cases and their close contacts were retrospectively collected from the outbreak that occurred in Guangdong, China in May and June 2021. Key epidemiological parameters, temporal trend of viral loads and secondary attack rates were estimated. We also evaluated the association of vaccination with viral load and transmission. Results We identified 167 patients infected with the Delta variant in the Guangdong outbreak. Mean estimates of latent and incubation period were 3.9 days and 5.8 days, respectively. Relatively higher viral load was observed in infections with Delta than in infections with wild-type SARS-CoV-2. Secondary attack rate among close contacts of cases with Delta was 1.4%, and 73.1% (95% credible interval (CrI): 32.9–91.4) of the transmissions occurred before onset. Index cases without vaccination (adjusted odds ratio (aOR): 2.84; 95% CI: 1.19–8.45) or with an incomplete vaccination series (aOR: 6.02; 95% CI: 2.45–18.16) were more likely to transmit infection to their contacts than those who had received the complete primary vaccination series. Discussion Patients infected with the Delta variant had more rapid symptom onset compared with the wild type. The time-varying serial interval should be accounted for in estimation of reproduction numbers. The higher viral load and higher risk of pre-symptomatic transmission indicated the challenges in control of infections with the Delta variant.
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Affiliation(s)
- Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Hualei Xin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jun Yuan
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Zimian Liang
- Foshan Center for Disease Control and Prevention, Foshan, Guangdong, China
| | - Jiayi Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Ting Hu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Eric Hy Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Yingtao Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
| | - Yan Li
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region, China
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13
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Lin Y, Yang B, Cobey S, Lau EHY, Adam DC, Wong JY, Bond HS, Cheung JK, Ho F, Gao H, Ali ST, Leung NHL, Tsang TK, Wu P, Leung GM, Cowling BJ. Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission. Nat Commun 2022; 13:1155. [PMID: 35241662 PMCID: PMC8894407 DOI: 10.1038/s41467-022-28812-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/14/2022] [Indexed: 12/20/2022] Open
Abstract
Many locations around the world have used real-time estimates of the time-varying effective reproductive number (\documentclass[12pt]{minimal}
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\begin{document}$${R}_{t}$$\end{document}Rt) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of \documentclass[12pt]{minimal}
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\begin{document}$${R}_{t}$$\end{document}Rt are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the latent period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of \documentclass[12pt]{minimal}
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\begin{document}$${R}_{t}$$\end{document}Rt based on case counts. We demonstrate that cycle threshold values could be used to improve real-time \documentclass[12pt]{minimal}
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\begin{document}$${R}_{t}$$\end{document}Rt estimation, enabling more timely tracking of epidemic dynamics. The time-varying effective reproductive number (Rt) is useful for monitoring transmission of infections such as COVID-19, but reporting delays impact case count-based estimation methods. Here, the authors demonstrate and validate a method for estimation of Rt based on viral load data from Hong Kong that does not require accurate daily counts.
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Affiliation(s)
- Yun Lin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Dillon C Adam
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jessica Y Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Helen S Bond
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Justin K Cheung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Faith Ho
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Huizhi Gao
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Nancy H L Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China. .,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China.
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14
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Abstract
Influenza is a common respiratory infection that causes considerable morbidity and mortality worldwide each year. In recent years, along with the improvement in computational resources, there have been a number of important developments in the science of influenza surveillance and forecasting. Influenza surveillance systems have been improved by synthesizing multiple sources of information. Influenza forecasting has developed into an active field, with annual challenges in the United States that have stimulated improved methodologies. Work continues on the optimal approaches to assimilating surveillance data and information on relevant driving factors to improve estimates of the current situation (nowcasting) and to forecast future dynamics.
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Affiliation(s)
- Sheikh Taslim Ali
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China;
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China;
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15
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Abstract
We estimated mean serial interval and superspreading potential for the Delta variant of severe acute respiratory syndrome coronavirus 2 in South Korea. Intervals were similar for the first (3.7 days) and second (3.5 days) study periods. Risk for superspreading events was also similar; 23% and 25% of cases, respectively, seeded 80% of transmissions.
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16
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Ryu S, Hwang Y, Ali ST, Kim DS, Klein EY, Lau EHY, Cowling BJ. Decreased Use of Broad-Spectrum Antibiotics During the Coronavirus Disease 2019 Epidemic in South Korea. J Infect Dis 2021; 224:949-955. [PMID: 33856455 PMCID: PMC8083342 DOI: 10.1093/infdis/jiab208] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 04/13/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Early in the coronavirus disease 2019 (COVID-19) pandemic, there was a concern over possible increase in antibiotic use due to coinfections among COVID-19 patients in the community. Here, we evaluate the changes in nationwide use of broad-spectrum antibiotics during the COVID-19 epidemic in South Korea. METHODS We obtained national reimbursement data on the prescription of antibiotics, including penicillin with β-lactamase inhibitors, cephalosporins, fluoroquinolones, and macrolides. We examined the number of antibiotic prescriptions compared with the previous 3 years in the same period from August to July. To quantify the impact of the COVID-19 epidemic on antibiotic use, we developed a regression model adjusting for changes of viral acute respiratory tract infections (ARTIs), which are an important factor driving antibiotic use. RESULTS During the COVID-19 epidemic in South Korea, the broad-spectrum antibiotic use dropped by 15%-55% compared to the previous 3 years. Overall reduction in antibiotic use adjusting for ARTIs was estimated to be 14%-30%, with a larger impact in children. CONCLUSIONS Our study found that broad-spectrum antibiotic use was substantially reduced during the COVID-19 epidemic in South Korea. This reduction can be in part due to reduced ARTIs as a result of stringent public health interventions including social distancing measures.
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Affiliation(s)
- Sukhyun Ryu
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, Republic of Korea
| | - Youngsik Hwang
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, Republic of Korea
| | - Sheikh Taslim Ali
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Dong-Sook Kim
- Pharmaceutical and Medical Technology Research Team, Department of Research, Health Insurance Review and Assessment Service, Wonju, South Korea
| | - Eili Y Klein
- Center for Disease Dynamics, Economics and Policy, Washington, District of Columbia, USA
- Johns Hopkins University, Baltimore, Maryland, USA
| | - Eric H Y Lau
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
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17
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Lau YC, Tsang TK, Kennedy-Shaffer L, Kahn R, Lau EHY, Chen D, Wong JY, Ali ST, Wu P, Cowling BJ. Joint Estimation Of Generation Time And Incubation Period For Coronavirus Disease (Covid-19). J Infect Dis 2021; 224:1664-1671. [PMID: 34423821 PMCID: PMC8499762 DOI: 10.1093/infdis/jiab424] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/21/2021] [Indexed: 01/02/2023] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) has caused a heavy disease burden globally. The impact of process and timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Because infection times are typically unobserved, there are relatively few estimates of generation time distribution. Methods We developed a statistical framework to jointly estimate generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of presymptomatic transmission, and basic reproduction number (R0) for COVID-19. Results The estimated mean incubation period was 4.8 days (95% confidence interval [CI], 4.1–5.6), and mean generation time was 5.7 days (95% CI, 4.8–6.5). The estimated R0 based on the estimated generation time was 2.2 (95% CI, 1.9–2.4). A simulation study suggested that our approach could provide unbiased estimates, insensitive to the width of exposure windows. Conclusions Properly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. R0 can be biased when it is derived based on serial interval as the proxy of generation time.
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Affiliation(s)
- Yiu Chung Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Lee Kennedy-Shaffer
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States.,Department of Mathematics and Statistics, Vassar College, Poughkeepsie, New York, United States
| | - Rebecca Kahn
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Dongxuan Chen
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Jessica Y Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
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18
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Ryu S, Ali ST, Noh E, Kim D, Lau EHY, Cowling BJ. Correction to: Transmission dynamics and control of two epidemic waves of SARS-CoV-2 in South Korea. BMC Infect Dis 2021; 21:660. [PMID: 34233629 PMCID: PMC8262583 DOI: 10.1186/s12879-021-06358-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Sukhyun Ryu
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, 35365, Republic of Korea.
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Hong Kong, Hong Kong, Special Administrative Region of China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, Special Administrative Region of China
| | - Eunbi Noh
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, 35365, Republic of Korea.,Graduate School of Public Health, Seoul National University, Seoul, 08826, Republic of Korea
| | - Dasom Kim
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, 35365, Republic of Korea
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Hong Kong, Hong Kong, Special Administrative Region of China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, Special Administrative Region of China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Hong Kong, Hong Kong, Special Administrative Region of China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, Special Administrative Region of China
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19
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Xin H, Wong JY, Murphy C, Yeung A, Ali ST, Wu P, Cowling BJ. The incubation period distribution of coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Clin Infect Dis 2021; 73:2344-2352. [PMID: 34117868 DOI: 10.1093/cid/ciab501] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Indexed: 11/14/2022] Open
Abstract
Incubation period is an important parameter to inform quarantine period and to study transmission dynamics of infectious diseases. We conducted a systematic review and meta-analysis on published estimates of the incubation period distribution of COVID-19, and showed that the pooled median of the point estimates of the mean, median and 95 th percentile for incubation period are 6.3 days (range: 1.8 to 11.9 days), 5.4 days (range: 2.0 to 17.9 days) and 13.1 days (range: 3.2 to 17.8 days) respectively. Estimates of the mean and 95 th percentile of the incubation period distribution were considerably shorter before the epidemic peak in China compared to after the peak, and variation was also noticed for different choices of methodological approach in estimation. Our findings implied that corrections may be needed before directly applying estimates of incubation period into control of or further studies on emerging infectious diseases.
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Affiliation(s)
- Hualei Xin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jessica Y Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Caitriona Murphy
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Amy Yeung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong
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20
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Ali ST, Yeung A, Shan S, Wang L, Gao H, Du Z, Xu XK, Wu P, Lau EHY, Cowling BJ. Serial intervals and case isolation delays for COVID-19: a systematic review and meta-analysis. Clin Infect Dis 2021; 74:685-694. [PMID: 34037748 PMCID: PMC8241473 DOI: 10.1093/cid/ciab491] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Indexed: 01/19/2023] Open
Abstract
Background Estimates of the serial interval distribution contribute to our understanding
of the transmission dynamics of coronavirus disease 2019 (COVID-19). Here,
we aimed to summarize the existing evidence on serial interval distributions
and delays in case isolation for COVID-19. Methods We conducted a systematic review of the published literature and preprints in
PubMed on two epidemiological parameters namely serial intervals and delay
intervals relating to isolation of cases for COVID-19 until 22 October, 2020
following predefined eligibility criteria. We assessed the variation in
these parameter estimates by correlation and regression analysis. Results Of 103 unique studies identified on serial intervals of COVID-19, 56 were
included providing 129 estimates and of 451 unique studies on isolation
delays, 18 studies were included providing 74 estimates. Serial interval
estimates varied from 1.0 to 9.9 days, while case isolation delays varied
from 1.0 to 12.5 days which were associated with spatial, methodological and
temporal factors. In mainland China, the pooled mean serial interval was 6.2
(range, 5.1-7.8) days before the epidemic peak and reduced to 4.9 (range,
1.9-6.5) days after the epidemic peak. Similarly, the pooled mean isolation
delay related intervals were 6.0 (range, 2.9-12.5) days and 2.4 (range,
2.0-2.7) days before and after the epidemic peak, respectively. There was a
positive association between serial interval and case isolation delay. Conclusions Temporal factors, such as different control measures and case isolation in
particular led to shorter serial interval estimates over time. Correcting
transmissibility estimates for these time-varying distributions could aid
mitigation efforts.
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Affiliation(s)
- Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Amy Yeung
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
| | - Songwei Shan
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Lin Wang
- Department of Genetics, University of
Cambridge, Cambridge CB2 3EH, UK
| | - Huizhi Gao
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Xiao-Ke Xu
- College of Information and Communication Engineering,
Dalian Minzu University, Dalian 116600, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease
Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of
Medicine, The University of Hong Kong, Hong Kong Special
Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science
and Technology Park, Hong Kong Special Administrative Region,
China
- Corresponding author: Prof. Benjamin J Cowling, School of Public
Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon
Road, Pokfulam, Hong Kong. Tel: +852 3917 6711; Fax: +852 3520 1945;
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Martín-Sánchez M, Lim WW, Yeung A, Adam DC, Ali ST, Lau EHY, Wu P, Yuen KY, Leung GM, Cowling BJ. COVID-19 transmission in Hong Kong despite universal masking. J Infect 2021; 83:92-95. [PMID: 33895227 PMCID: PMC8061183 DOI: 10.1016/j.jinf.2021.04.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 04/18/2021] [Indexed: 12/23/2022]
Abstract
Objectives mask-wearing outside the home has been almost universal in Hong Kong since late January 2020 with very high compliance. Nevertheless, community spread of COVID-19 has still occurred. We aimed to assess the settings where COVID-19 transmission occurred and determine the fraction of transmission events that occurred in settings where masks are not usually worn. Methods we reviewed detailed information provided by the Hong Kong Department of Health on local COVID-19 cases diagnosed up to 30 September 2020 to determine the most likely settings in which transmission occurred. We classified them in probably mask-on or mask-of and compared the prevalence of asymptomatic infections in these settings. Results among the 2425 cases (65.3%, 2425/3711) with information on transmission setting, 77.6% of the transmission occurred in household and social settings where face masks are not usually worn. Infections that occurred in mask-on settings were more likely to be asymptomatic (adjusted odds ratio 1.33; 95% confidence interval: 1.04, 1.68). Conclusions we conclude that universal mask-wearing can reduce transmission, but transmission can continue to occur in settings where face masks are not usually worn. The higher proportion of asymptomatic cases in mask-on settings could be related to a milder disease presentation or earlier case detection.
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Affiliation(s)
- Mario Martín-Sánchez
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Wey Wen Lim
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Amy Yeung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Dillon C Adam
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Kwok-Yung Yuen
- State Key Laboratory for Emerging Infectious Diseases, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Pokfulam, China; Department of Microbiology, Carol Yu Centre for Infection, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Pokfulam, China; Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, Pokfulam, China; Department of Clinical Microbiology and Infection Control, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
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22
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Du Z, Wang L, Yang B, Ali ST, Tsang TK, Shan S, Wu P, Lau EHY, Cowling BJ, Meyers LA. Risk for International Importations of Variant SARS-CoV-2 Originating in the United Kingdom. Emerg Infect Dis 2021; 27:1527-1529. [PMID: 33760727 PMCID: PMC8084518 DOI: 10.3201/eid2705.210050] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
A fast-spreading severe acute respiratory syndrome coronavirus 2 variant identified in the United Kingdom in December 2020 has raised international alarm. We analyzed data from 15 countries and estimated that the chance that this variant was imported into these countries by travelers from the United Kingdom by December 7 is >50%.
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23
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Du Z, Wang L, Yang B, Ali ST, Tsang TK, Shan S, Wu P, Lau EHY, Cowling BJ, Meyers LA. International risk of the new variant COVID-19 importations originating in the United Kingdom.. [PMID: 33469591 PMCID: PMC7814837 DOI: 10.1101/2021.01.09.21249384] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
A fast-spreading SARS-CoV-2 variant identified in the United Kingdom in December 2020 has raised international alarm. We estimate that, in all 15 countries analyzed, there is at least a 50% chance the variant was imported by travelers from the United Kingdom by December 7th.
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24
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Xu XK, Liu XF, Wu Y, Ali ST, Du Z, Bosetti P, Lau EHY, Cowling BJ, Wang L. Reconstruction of Transmission Pairs for Novel Coronavirus Disease 2019 (COVID-19) in Mainland China: Estimation of Superspreading Events, Serial Interval, and Hazard of Infection. Clin Infect Dis 2020; 71:3163-3167. [PMID: 32556265 PMCID: PMC7337632 DOI: 10.1093/cid/ciaa790] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 06/11/2020] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Knowledge on the epidemiological features and transmission patterns of novel coronavirus disease (COVID-19) is accumulating. Detailed line-list data with household settings can advance the understanding of COVID-19 transmission dynamics. METHODS A unique database with detailed demographic characteristics, travel history, social relationships, and epidemiological timelines for 1407 transmission pairs that formed 643 transmission clusters in mainland China was reconstructed from 9120 COVID-19 confirmed cases reported during 15 January-29 February 2020. Statistical model fittings were used to identify the superspreading events and estimate serial interval distributions. Age- and sex-stratified hazards of infection were estimated for household vs nonhousehold transmissions. RESULTS There were 34 primary cases identified as superspreaders, with 5 superspreading events occurred within households. Mean and standard deviation of serial intervals were estimated as 5.0 (95% credible interval [CrI], 4.4-5.5) days and 5.2 (95% CrI, 4.9-5.7) days for household transmissions and 5.2 (95% CrI, 4.6-5.8) and 5.3 (95% CrI, 4.9-5.7) days for nonhousehold transmissions, respectively. The hazard of being infected outside of households is higher for people aged 18-64 years, whereas hazard of being infected within households is higher for young and old people. CONCLUSIONS Nonnegligible frequency of superspreading events, short serial intervals, and a higher risk of being infected outside of households for male people of working age indicate a significant barrier to the identification and management of COVID-19 cases, which requires enhanced nonpharmaceutical interventions to mitigate this pandemic.
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Affiliation(s)
- Xiao-Ke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Dalian, China
| | - Xiao Fan Liu
- Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ye Wu
- Computational Communication Research Center, Beijing Normal University, Zhuhai, China
- School of Journalism and Communication, Beijing Normal University, Beijing, China
| | - Sheikh Taslim Ali
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Zhanwei Du
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA
| | - Paolo Bosetti
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Centre National de la Recherche Scientifique (CNRS), Paris, France
| | - Eric H Y Lau
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Centre National de la Recherche Scientifique (CNRS), Paris, France
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
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25
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Abstract
Meteorological drivers are known to affect transmissibility of respiratory viruses including respiratory syncytial virus (RSV), but there are few studies quantifying the role of these drivers. We used daily RSV hospitalization data to estimate the daily effective reproduction number (Rt), a real-time measure of transmissibility, and examined its relationship with environmental drivers in Singapore from 2005 through 2015. We used multivariable regression models to quantify the proportion of the variance in Rt explained by each meteorological driver. After constructing a basic model for RSV seasonality, we found that by adding meteorological variables into this model we were able to explain a further 15% of the variance in RSV transmissibility. Lower and higher value of mean temperature, diurnal temperature range (DTR), precipitation and relative humidity were associated with increased RSV transmissibility, while higher value of maximum wind speed was correlated with decreased RSV transmissibility. We found that a number of meteorological drivers were associated with RSV transmissibility. While indoor conditions may differ from ambient outdoor conditions, our findings are indicative of a role of ambient temperature, humidity and wind speed in affecting RSV transmission that could be biological or could reflect indirect effects via the consequences on time spent indoors.
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Affiliation(s)
- Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong Special Administrative Region, China
| | - Clarence C Tam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, Singapore, 117549, Singapore.
- London School of Hygiene and Tropical Medicine, London, UK.
| | - 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, Pok Fu Lam, Hong Kong Special Administrative Region, China
| | - Kee Thai Yeo
- Department of Neonatology, KK Women's and Children's Hospital, Singapore, Singapore
- Infectious Diseases Service, KK Women's and Children's Hospital, Singapore, Singapore
| | - Chee Fu Yung
- Department of Neonatology, KK Women's and Children's Hospital, Singapore, Singapore
- Infectious Diseases Service, KK Women's and Children's Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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26
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Ryu S, Ali ST, Cowling BJ, Lau EHY. Effects of School Holidays on Seasonal Influenza in South Korea, 2014-2016. J Infect Dis 2020; 222:832-835. [PMID: 32277239 PMCID: PMC7399705 DOI: 10.1093/infdis/jiaa179] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 04/09/2020] [Indexed: 12/22/2022] Open
Abstract
School closures are considered as a potential nonpharmaceutical intervention to mitigate severe influenza epidemics and pandemics. In this study, we assessed the effects of scheduled school closure on influenza transmission using influenza surveillance data before, during, and after spring breaks in South Korea, 2014-2016. During the spring breaks, influenza transmission was reduced by 27%-39%, while the overall reduction in transmissibility was estimated to be 6%-23%, with greater effects observed among school-aged children.
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Affiliation(s)
- Sukhyun Ryu
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, Republic of Korea
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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27
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Ali ST, Wang L, Lau EHY, Xu XK, Du Z, Wu Y, Leung GM, Cowling BJ. Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions. Science 2020; 369:1106-1109. [PMID: 32694200 PMCID: PMC7402628 DOI: 10.1126/science.abc9004] [Citation(s) in RCA: 210] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 07/13/2020] [Indexed: 12/13/2022]
Abstract
In epidemiology, serial intervals are measured from when one infected person starts to show symptoms to when the next person infected becomes symptomatic. For any specific infection, the serial interval is assumed to be a fixed characteristic. Using valuable transmission pair data for coronavirus disease (COVID-19) in mainland China, Ali et al. noticed that the average serial interval changed as nonpharmaceutical interventions were introduced. In mid-January 2020, serial intervals were on average 7.8 days, whereas in early February 2020, they decreased to an average of 2.2 days. The more quickly infected persons were identified and isolated, the shorter the serial interval became and the fewer the opportunities for virus transmission. The change in serial interval may not only measure the effectiveness of infection control interventions but may also indicate rising population immunity. Science, this issue p. 1106 Studies of novel coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have reported varying estimates of epidemiological parameters, including serial interval distributions—i.e., the time between illness onset in successive cases in a transmission chain—and reproduction numbers. By compiling a line-list database of transmission pairs in mainland China, we show that mean serial intervals of COVID-19 shortened substantially from 7.8 to 2.6 days within a month (9 January to 13 February 2020). This change was driven by enhanced nonpharmaceutical interventions, particularly case isolation. We also show that using real-time estimation of serial intervals allowing for variation over time provides more accurate estimates of reproduction numbers than using conventionally fixed serial interval distributions. These findings could improve our ability to assess transmission dynamics, forecast future incidence, and estimate the impact of control measures.
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Affiliation(s)
- Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK.,Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris 75015, France
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiao-Ke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China
| | - Zhanwei Du
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78705, USA
| | - Ye Wu
- School of Journalism and Communication, Beijing Normal University, Beijing 100875, China.,Computational Communication Research Center, Beijing Normal University, Zhuhai 519087, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
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28
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Abstract
Studies of novel coronavirus disease (COVID-19) have reported varying estimates of epidemiological parameters such as serial intervals and reproduction numbers. By compiling a unique line-list database of transmission pairs in mainland China, we demonstrated that serial intervals of COVID-19 have shortened substantially from a mean of 7.8 days to 2.6 days within a month. This change is driven by enhanced non-pharmaceutical interventions, in particular case isolation. We also demonstrated that using real-time estimation of serial intervals allowing for variation over time would provide more accurate estimates of reproduction numbers, than by using conventional definition of fixed serial interval distributions. These findings are essential to improve the assessment of transmission dynamics, forecasting future incidence, and estimating the impact of control measures.
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Affiliation(s)
| | | | | | | | | | - Ye Wu
- Beijing Normal University
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29
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Cowling BJ, Ali ST, Ng TWY, Tsang TK, Li JCM, Fong MW, Liao Q, Kwan MY, Lee SL, Chiu SS, Wu JT, Wu P, Leung GM. Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study. Lancet Public Health 2020; 5:e279-e288. [PMID: 32311320 DOI: 10.1101/2020.03.12.20034660] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/01/2020] [Accepted: 04/07/2020] [Indexed: 05/20/2023]
Abstract
BACKGROUND A range of public health measures have been implemented to suppress local transmission of coronavirus disease 2019 (COVID-19) in Hong Kong. We examined the effect of these interventions and behavioural changes of the public on the incidence of COVID-19, as well as on influenza virus infections, which might share some aspects of transmission dynamics with COVID-19. METHODS We analysed data on laboratory-confirmed COVID-19 cases, influenza surveillance data in outpatients of all ages, and influenza hospitalisations in children. We estimated the daily effective reproduction number (Rt) for COVID-19 and influenza A H1N1 to estimate changes in transmissibility over time. Attitudes towards COVID-19 and changes in population behaviours were reviewed through three telephone surveys done on Jan 20-23, Feb 11-14, and March 10-13, 2020. FINDINGS COVID-19 transmissibility measured by Rt has remained at approximately 1 for 8 weeks in Hong Kong. Influenza transmission declined substantially after the implementation of social distancing measures and changes in population behaviours in late January, with a 44% (95% CI 34-53%) reduction in transmissibility in the community, from an estimated Rt of 1·28 (95% CI 1·26-1·30) before the start of the school closures to 0·72 (0·70-0·74) during the closure weeks. Similarly, a 33% (24-43%) reduction in transmissibility was seen based on paediatric hospitalisation rates, from an Rt of 1·10 (1·06-1·12) before the start of the school closures to 0·73 (0·68-0·77) after school closures. Among respondents to the surveys, 74·5%, 97·5%, and 98·8% reported wearing masks when going out, and 61·3%, 90·2%, and 85·1% reported avoiding crowded places in surveys 1 (n=1008), 2 (n=1000), and 3 (n=1005), respectively. INTERPRETATION Our study shows that non-pharmaceutical interventions (including border restrictions, quarantine and isolation, distancing, and changes in population behaviour) were associated with reduced transmission of COVID-19 in Hong Kong, and are also likely to have substantially reduced influenza transmission in early February, 2020. FUNDING Health and Medical Research Fund, Hong Kong.
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Affiliation(s)
- Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Tiffany W Y Ng
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Julian C M Li
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Min Whui Fong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Qiuyan Liao
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Mike Yw Kwan
- Department of Paediatrics and Adolescent Medicine, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - So Lun Lee
- Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital and Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Susan S Chiu
- Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital and Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China.
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
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30
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Ryu S, Ali ST, Lim JS, Chun BC. Estimation of the Excess COVID-19 Cases in Seoul, South Korea by the Students Arriving from China. Int J Environ Res Public Health 2020; 17:E3113. [PMID: 32365703 PMCID: PMC7246702 DOI: 10.3390/ijerph17093113] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 04/17/2020] [Accepted: 04/25/2020] [Indexed: 01/04/2023]
Abstract
Background: In March 2020, overall, 37,000 international students from China, a country at risk of the 2019-novel coronavirus (COVID-19) infection has arrived in Seoul, South Korea. Individuals from the country at risk of COVID-19 infection have been included in the Korean home-quarantine program, but the efficacy of the program is uncertain. Methods: To estimate the possible number of infected individuals within the large influx of international students from China, we used a deterministic compartmental model for epidemic and performed a simulation-based search of different rates of compliance with home-quarantine. Results: Under the home-quarantine program, the number of the infected individuals would reach 40-72 from 12 March-24 March with the arrival of 0.2% of pre-infectious individuals. Furthermore, the number of isolated individuals would peak at 40-64 from 13 March-27 March in Seoul, South Korea. Our findings indicated when incoming international students showed strict compliance with quarantine, epidemics by the international student from China were less likely to occur in Seoul, South Korea. Conclusions: To mitigate possible epidemics, additional efforts to improve the compliance of home-quarantine of the individuals from countries with the virus risk are warranted along with other containment policies.
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Affiliation(s)
- Sukhyun Ryu
- Department of Preventive Medicine, College of Medicine, Konyang University, Daejeon 35365, Korea;
- Korean Society of Epidemiology 2019-nCoV Task Force Team, Korea
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China;
| | - Jun-Sik Lim
- College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University, Chuncheon 24341, Korea;
| | - Byung Chul Chun
- Korean Society of Epidemiology 2019-nCoV Task Force Team, Korea
- Department of Preventive Medicine, Korea University College of Medicine, Seoul 02841, Korea
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31
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Cowling BJ, Ali ST, Ng TWY, Tsang TK, Li JCM, Fong MW, Liao Q, Kwan MY, Lee SL, Chiu SS, Wu JT, Wu P, Leung GM. Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study. Lancet Public Health 2020; 5:e279-e288. [PMID: 32311320 PMCID: PMC7164922 DOI: 10.1016/s2468-2667(20)30090-6] [Citation(s) in RCA: 713] [Impact Index Per Article: 178.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/01/2020] [Accepted: 04/07/2020] [Indexed: 02/03/2023]
Abstract
Background A range of public health measures have been implemented to suppress local transmission of coronavirus disease 2019 (COVID-19) in Hong Kong. We examined the effect of these interventions and behavioural changes of the public on the incidence of COVID-19, as well as on influenza virus infections, which might share some aspects of transmission dynamics with COVID-19. Methods We analysed data on laboratory-confirmed COVID-19 cases, influenza surveillance data in outpatients of all ages, and influenza hospitalisations in children. We estimated the daily effective reproduction number (Rt) for COVID-19 and influenza A H1N1 to estimate changes in transmissibility over time. Attitudes towards COVID-19 and changes in population behaviours were reviewed through three telephone surveys done on Jan 20–23, Feb 11–14, and March 10–13, 2020. Findings COVID-19 transmissibility measured by Rt has remained at approximately 1 for 8 weeks in Hong Kong. Influenza transmission declined substantially after the implementation of social distancing measures and changes in population behaviours in late January, with a 44% (95% CI 34–53%) reduction in transmissibility in the community, from an estimated Rt of 1·28 (95% CI 1·26–1·30) before the start of the school closures to 0·72 (0·70–0·74) during the closure weeks. Similarly, a 33% (24–43%) reduction in transmissibility was seen based on paediatric hospitalisation rates, from an Rt of 1·10 (1·06–1·12) before the start of the school closures to 0·73 (0·68–0·77) after school closures. Among respondents to the surveys, 74·5%, 97·5%, and 98·8% reported wearing masks when going out, and 61·3%, 90·2%, and 85·1% reported avoiding crowded places in surveys 1 (n=1008), 2 (n=1000), and 3 (n=1005), respectively. Interpretation Our study shows that non-pharmaceutical interventions (including border restrictions, quarantine and isolation, distancing, and changes in population behaviour) were associated with reduced transmission of COVID-19 in Hong Kong, and are also likely to have substantially reduced influenza transmission in early February, 2020. Funding Health and Medical Research Fund, Hong Kong.
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Affiliation(s)
- Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Tiffany W Y Ng
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Julian C M Li
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Min Whui Fong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Qiuyan Liao
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Mike Yw Kwan
- Department of Paediatrics and Adolescent Medicine, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - So Lun Lee
- Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital and Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Susan S Chiu
- Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital and Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China.
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China
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Xu XK, Liu XF, Wang L, Ali ST, Du Z, Bosetti P, Cowling BJ, Wu Y. Household transmissions of SARS-CoV-2 in the time of unprecedented travel lockdown in China. medRxiv 2020:2020.03.02.20029868. [PMID: 32511615 PMCID: PMC7276042 DOI: 10.1101/2020.03.02.20029868] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Importance Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in the city of Wuhan, China, in December 2019 and then spread globally. Limited information is available for characterizing epidemiological features and transmission patterns in the regions outside of Hubei Province. Detailed data on transmission at the individual level could be an asset to understand the transmission mechanisms and respective patterns in different settings. Objective To reconstruct infection events and transmission clusters of SARS-CoV-2 for estimating epidemiological characteristics at household and non-household settings, including super-spreading events, serial intervals, age- and gender-stratified risks of infection in China outside of Hubei Province. Design Setting and Participants 9,120 confirmed cases reported online by 264 Chinese urban Health Commissions in 27 provinces from January 20 to February 19, 2020. A line-list database is established with detailed information on demographic, social and epidemiological characteristics. The infection events are categorized into the household and non-household settings. Exposures Confirmed cases of SARS-CoV-2 infections. Main Outcomes and Measures Information about demographic characteristics, social relationships, travel history, timelines of potential exposure, symptom onset, confirmation, and hospitalization were extracted from online public reports. 1,407 infection events formed 643 transmission clusters were reconstructed. Results In total 34 primary cases were identified as super spreaders, and 5 household super-spreading events were observed. The mean serial interval is estimated to be 4.95 days (standard deviation: 5.24 days) and 5.19 days (standard deviation: 5.28 days) for households and non-household transmissions, respectively. The risk of being infected outside of households is higher for age groups between 18 and 64 years, whereas the hazard of being infected within households is higher for age groups of young (<18) and elderly (>65) people. Conclusions and Relevance The identification of super-spreading events, short serial intervals, and a higher risk of being infected outside of households for male people of age between 18 and 64 indicate a significant barrier to the case identification and management, which calls for intensive non-pharmaceutical interventions (e.g. cancellation of public gathering, limited access of public services) as the potential mitigation strategies.
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Affiliation(s)
- Xiao-Ke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China
| | - Xiao-Fan Liu
- Web Mining Lab, Department of Media and Communication, City University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Global Health, Institut Pasteur, UMR2000, CNRS, Paris 75015, France
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Zhanwei Du
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas 78705, USA
| | - Paolo Bosetti
- Department of Global Health, Institut Pasteur, UMR2000, CNRS, Paris 75015, France
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ye Wu
- Computational Communication Research Center, Beijing Normal University, Zhuhai, 519087, China
- School of Journalism and Communication, Beijing Normal University, Beijing, 100875, China
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Abstract
In winter 2018, schools in Hong Kong were closed 1 week before the scheduled Chinese New Year holiday to mitigate an influenza B virus epidemic. The intervention occurred after the epidemic peak and reduced overall incidence by ≈4.2%. School-based vaccination programs should be implemented to more effectively reduce influenza illnesses.
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Ali ST, Wu P, Cauchemez S, He D, Fang VJ, Cowling BJ, Tian L. Ambient ozone and influenza transmissibility in Hong Kong. Eur Respir J 2018; 51:13993003.00369-2018. [PMID: 29563172 DOI: 10.1183/13993003.00369-2018] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 02/27/2018] [Indexed: 02/02/2023]
Affiliation(s)
- Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.,Centre National de la Recherche Scientifique, URA3012, Paris, France.,Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France
| | - Daihai He
- Dept of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Vicky J Fang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Linwei Tian
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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Abstract
The treatment of rosacea is challenging because several pathophysiologic processes may be involved, including neurovascular dysregulation and alterations in innate immune status. Demodex mites may play a role in the latter mechanism. Topical ivermectin is a new therapeutic modality which demonstrates antiparasitic and anti-inflammatory properties. This article reviews published evidence related to the efficacy and safety of topical ivermectin. PubMed was utilized to search for key words "topical ivermectin", "ivermectin cream" and "rosacea". Three clinical trials were found that studied topical ivermectin as a treatment option for rosacea. Ivermectin was effective, safe and well tolerated.
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Affiliation(s)
- S T Ali
- Center for Dermatology Research, Department of Dermatology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - H Alinia
- Center for Dermatology Research, Department of Dermatology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
| | - S R Feldman
- Center for Dermatology Research, Department of Dermatology; Department of Pathology; Departments of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
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Beacham TA, Macia VM, Rooks P, White DA, Ali ST. Altered lipid accumulation in Nannochloropsis salina CCAP849/3 following EMS and UV induced mutagenesis. ACTA ACUST UNITED AC 2015; 7:87-94. [PMID: 26753128 PMCID: PMC4691955 DOI: 10.1016/j.btre.2015.05.007] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Revised: 05/15/2015] [Accepted: 05/29/2015] [Indexed: 11/24/2022]
Abstract
EMS and UV mutagenesis of Nannochloropsis salina combined with FACS for mutant enrichment. Productivity of EMS mutants increased by 76% and showed range of FA profile changes. Dual EMS and UV mutants accumulated 3 fold more lipid than the wild type. Elevation in lipid content comes with a cost to growth rate impacting productivity. Mutants suitable for divergent industries generated (biofuel, high value PUFA production).
Microalgae have potential as a chemical feed stock in a range of industrial applications. Nannochloropsis salina was subject to EMS mutagenesis and the highest lipid containing cells selected using fluorescence-activated cell sorting. Assessment of growth, lipid content and fatty acid composition identified mutant strains displaying a range of altered traits including changes in the PUFA content and a total FAME increase of up to 156% that of the wild type strain. Combined with a reduction in growth this demonstrated a productivity increase of up to 76%. Following UV mutagenesis, lipid accumulation of the mutant cultures was elevated to more than 3 fold that of the wild type strain, however reduced growth rates resulted in a reduction in overall productivity. Changes observed are indicative of alterations to the regulation of the omega 6 Kennedy pathway. The importance of these variations in physiology for industrial applications such as biofuel production is discussed.
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Affiliation(s)
- T A Beacham
- Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, Devon PL1 3DH, UK
| | - V Mora Macia
- Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, Devon PL1 3DH, UK; Plymouth University, Drake Circus, Plymouth, PL4 8AA, UK
| | - P Rooks
- Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, Devon PL1 3DH, UK
| | - D A White
- Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, Devon PL1 3DH, UK; Plymouth University, Drake Circus, Plymouth, PL4 8AA, UK
| | - S T Ali
- Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, Devon PL1 3DH, UK
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Ali ST, Kadi AS, Ferguson NM. Transmission dynamics of the 2009 influenza A (H1N1) pandemic in India: the impact of holiday-related school closure. Epidemics 2013; 5:157-63. [PMID: 24267871 DOI: 10.1016/j.epidem.2013.08.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2013] [Revised: 08/08/2013] [Accepted: 08/15/2013] [Indexed: 11/30/2022] Open
Abstract
The role of social-distancing measures, such as school closures, is a controversial aspect of pandemic mitigation planning. However, the timing of 2009 pandemic provides a natural experiment for evaluating the impact of school closure during holidays on influenza transmission. To quantify the transmission intensity of the influenza A (H1N1) pdm'09 in India, by estimating the time varying reproduction number (Rt) and correlating the temporal changes in the estimates of Rt for different regions of India with the timing of school holidays. We used daily lab-confirmed case reports of influenza A (H1N1) pdm'09 in India (during 17 May'09 to 17 May'10), stratified by regions. We estimated the transmissibility of the pandemic for different regions from these time-series, using Bayesian methods applied to a branching process model of disease spread and correlated the resulting estimates with the timing of school holidays in each region. The North-west region experienced two notable waves, with the peak of the first wave coinciding with the start of a 4 week school holiday (September-October'09). In the southern region the two waves were less clear cut, though again the first peak of the first wave coincided with the start of school holidays--albeit of less than 2 weeks duration (August'09). Our analysis suggests that the school holidays had a significant influence on the epidemiology of the 2009 pandemic in India. We estimate that school holidays reduced the reproduction number by 14-27% in different regions of India, relative to levels seen outside holiday periods. The estimates of the reproduction number obtained (with peak R values below 1.5) are compatible with those reported from other regions of the world. This work reinforces past studies showing the significant impact of school holidays on spread of 2009 pandemic virus, and by inference the role of contact patterns in children on transmission.
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Affiliation(s)
- Sheikh Taslim Ali
- MRC Centre of Outbreak Analysis and Modelling, Department of Infectious Diseases Epidemiology, Imperial College London, London, UK; Department of Studies in Statistics, Karnatak University, Dharwad 580003, India.
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White DA, Hird LC, Ali ST. Production and characterization of a trehalolipid biosurfactant produced by the novel marine bacterium Rhodococcus sp., strain PML026. J Appl Microbiol 2013; 115:744-55. [PMID: 23789786 DOI: 10.1111/jam.12287] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Revised: 06/13/2013] [Accepted: 06/16/2013] [Indexed: 11/30/2022]
Abstract
AIMS The aim of this study was to evaluate biosurfactant production by a novel marine Rhodococcus sp., strain PML026 and characterize the chemical nature and properties of the biosurfactant. METHODS AND RESULTS A novel marine bacterium (Rhodococcus species; strain PML026) was shown to produce biosurfactant in the presence of hydrophobic substrate (sunflower oil). Biosurfactant production (identified as a trehalolipid) was monitored in whole-batch cultures (oil layer and aqueous phase), aqueous phase (no oil layer) and filtered (0·2 μm) aqueous phase (no oil or cells; extracellular) and was shown to be closely associated with growth/biomass production. Extracellular trehalolipid levels increased postonset of stationary growth phase. Purified trehalolipid was able to reduce the surface tension of water to 29 mN m(-1) at Critical Micellar Concentration (CMC) of c. 250 mg l(-1) and produced emulsions that were stable to a wide range of conditions (pH 2-10, temperatures of 20-100°C and NaCl concentrations of 5-25% w/v). Separate chemical analyses of the intact trehalolipid and its constituents demonstrated the compound was in fact a mixture of homologues (>1180 MW) consisting of a trehalose moiety esterified to a series of straight chain and hydroxylated fatty acids. CONCLUSIONS The trehalolipid biosurfactant produced by the novel marine strain Rhodococcus sp. PML026 was characterized and exhibited high surfactant activity under a wide range of conditions. SIGNIFICANCE AND IMPACT OF STUDY Strain PML026 of Rhodococcus sp. is a potential candidate for bioremediation or biosurfactant production for various applications.
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Affiliation(s)
- D A White
- Plymouth Marine Laboratory, Plymouth, Devon, UK.
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39
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Ali ST. Post Ejaculatory Effects of Sildenafil Citrate (Viagra) On Sexual Responses In Diabetic Neuropathic Men. Sud Jnl Med Sci 2008. [DOI: 10.4314/sjms.v2i4.38499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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40
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Abstract
Studies on the chemical constituents of the aerial part of Centella asiatica have led to the isolation of three new compounds, named centellin (1), asiaticin (2), and centellicin (3). Their structures have been elucidated through spectral studies including 2D NMR experiments (HMQC, HMBC, (1)H-(1)H COSY, NOESY and J resolved).
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Affiliation(s)
- B S Siddiqui
- H.E.J. Research Institute of Chemistry, International Centre for Chemical Sciences, University of Karachi, Karachi 75270, Pakistan.
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41
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Abstract
Three compounds, pubadysone [11 alpha-hydroxy-18,20-oxido-3-oxo-pregna-1,4,17(20)-triene] (1), puboestrene [3-acetoxy-17-oxo-1,3,5(10)-estratriene] (2) and pubamide [3,18-dioxo-11 alpha-hydroxycona-1,4-diene] (3), have been isolated from the bark of Holarrhena pubescens. Their structures have been established through spectroscopic studies.
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Affiliation(s)
- B S Siddiqui
- H.E.J. Research Institute of Chemistry, International Centre for Chemical Sciences, University of Karachi, Karachi 75270, Pakistan.
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42
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Abstract
Following a general procedure developed previously [Ann. Henri Poincaré 1, 685 (2000)], here we construct Wigner functions on a phase space related to the similitude group in two dimensions. Since the group space in this case is topologically homeomorphic to the phase space in question, the Wigner functions so constructed may also be considered as being functions on the group space itself. Previously the similitude group was used to construct wavelets for two-dimensional image analysis; we discuss here the connection between the wavelet transform and the Wigner function.
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Affiliation(s)
- S T Ali
- Department of Mathematics and Statistics, Concordia University, Montréal, Québec, Canada.
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43
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Ali ST. Measurement of the residual urine index in insulin-dependent and non-insulin dependent diabetic men with and without neuropathy. Acta Physiol Hung 1999; 85:243-50. [PMID: 10101538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Value of the residual urine index was evaluated in 40 individuals both insulin-dependent (IDDM) and non-insulin dependent (NIDDM) diabetic male patients with and without an objective evidence of neuropathy and in 20 age matched non-diabetic men serving as controls using post void bladder ultrasonographic technique. These studies revealed striking results in the neuropathic group. Both IDDM and NIDDM diabetic patients with neuropathy exhibited a significant (P < 0.005) increase in residual-volume in comparison with the controls of the same age group and a direct correlation between residual urine retention and neurogenic bladder was found to be established thus suggesting a generalized massive hypotonia of the bladder in these patients. However, non of the two types of non-neuropathic diabetic patients showed significant difference in the above-mentioned parameters compared to that their respective controls. A non-significant association in the values of the study parameters between insulin dependent and non-insulin dependent diabetic men (with and without neuropathy) was also observed. These findings thus suggest a probable neuropathic involvement in the pathway of urinary tract in both IDDM and NIDDM diabetic men with neuropathy. The greater impairment of the values of residual urine index in these patients may be due to overall greater severity of neuropathy with sympathetic as well as parasympathetic damage irrespective of their type of diabetes.
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Affiliation(s)
- S T Ali
- Department of Physiology, University of Karachi, Pakistan
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44
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Abstract
An in vitro normal human epidermal keratinocytes (NHEK) model was used to study and to characterize the protease stimulated by the mustards 2-chloroethyl ethyl sulphide (CEES), 2-chloro-N-(2-chloroethyl)-N-methylethanamine hydrochloride (nitrogen mustard, HN2), and Bis-2-chloroethyl sulfide (sulfur mustard, HD). The results obtained by using a chromozym (TRY) peptide substrate protease assay showed the optimum mustard concentration and time for protease stimulation to be about 200 microM CEES, 100 microM HN2 or HD, and 16 hours. The mustard-stimulated protease was membrane-bound, and was inhibited by adding a Ca2+ chelator EGTA (2 mM), BAPTA AM (50 microM) or a serine protease inhibitor diisopropyl fluoro-phosphate DFP (1 mM), or a protein synthesis inhibitor cycloheximide (10 microM) in the extracellular medium. These results suggest that one of the mechanisms of mustard toxicity is via the stimulation of a trypsin/chymotrypsin like serine protease, which is dependent on Ca2+ and new protein synthesis. Sodium dodecyl sulfate polyacrylamide gel electrophoresis revealed a mustard-stimulated approximately equal to 70-80 KDa protein band that was associated with protease activity which was inhibitable by EGTA, BAPTA, DFP or cycloheximide. This mustard-stimulated protein (protease) may serve as a diagnostic tool for mustard exposure as well as an assay for screening prospective antivesicant protease inhibitor drugs.
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Affiliation(s)
- P Ray
- Biology Department, Walter Reed Army Institute of Research, Washington, D. C. 20307-5100, USA
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45
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Abstract
Chemical cross-linking was used to study the interactions of the anti-cell-death protein Bcl2 with other proteins in the outer mitochondrial membrane. Cross-linking of mitochondrial surface proteins produced a large Bcl2-containing complex (>200 kDa), and a Bcl2-derived peptide was shown to cross-link specifically with a mitochondrial protein identified by immunoblotting as Raf-1 kinase.
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Affiliation(s)
- S T Ali
- Division of Biochemistry and Molecular Biology, Institute of Biomedical and Life Sciences, Joseph Black Building, University of Glasgow, Glasgow G12 8QQ, Scotland, U.K
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Green JD, Laue ED, Perham RN, Ali ST, Guest JR. Three-dimensional structure of a lipoyl domain from the dihydrolipoyl acetyltransferase component of the pyruvate dehydrogenase multienzyme complex of Escherichia coli. J Mol Biol 1995; 248:328-43. [PMID: 7739044 DOI: 10.1016/s0022-2836(95)80054-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The structure of a lipoyl domain from the pyruvate dehydrogenase multienzyme complex of Escherichia coli has been determined by means of nuclear magnetic resonance spectroscopy. A total of 549 nuclear Overhauser effect distance restraints, 52 phi torsion angle restraints and 16 slowly exchanging amide protons were employed as input for the structure calculations. These were performed using a combined distance geometry-simulated annealing strategy. The domain is a hybrid between the N and C-terminal halves of the first and third lipoyl domains, respectively, of the dihydrolipoyl acetyltransferase component of the E. coli multienzyme complex, representing residues 1 to 33 and 238 to 289 (wild-type numbering). The lipoyl-lysine residue was also replaced by glutamine. Nonetheless, its structure, two four-stranded beta-sheets forming a flattened beta-barrel, closely resembles that of the lipoyl domain from the pyruvate dehydrogenase multienzyme complex from Bacillus stearothermophilus determined previously. As before, the lipoylation site is physically exposed in a tight turn in one of the beta-sheets, and the N and C-terminal residues are close together at the other end of the molecule in adjacent strands of the other beta-sheet. Another prominently conserved feature of the structure is the 2-fold axis of quasi-symmetry relating the N and C-terminal halves of the domain. Consistent with the high level of sequence similarity between lipoyl domains of 2-oxo acid dehydrogenase multienzyme complexes from many different sources, these results confirm that all lipoyl domains are likely to have closely related structures.
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Affiliation(s)
- J D Green
- Cambridge Centre for Molecular Recognition, Department of Biochemistry, University of Cambridge, England
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Ali ST, Shaikh RN, Siddiqui NA, Raza PQ. Estimation of nor-adrenaline content of human penile tissue in diabeticmen with/without neuropathy. Pak J Pharm Sci 1994; 7:35-44. [PMID: 16414754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Penile tissue consisting of corps cavernosum (cc) and tunica albuginea (TA) was obtained from 35 impotent patients undergoing surgery for implantation of penile prostheses and was examined for nor adrenaline content. 10 patients were classified as a non diabetic non neuropathic group, on the basis of their clinical history and differential diagnostic symptoms which included Peyronie's disease, vascular disease, hypertension and psychogenic impotence. The nor adrenaline content was found to be significantly lower in tunica albuginea than the corpus cavernosum (P<0.02) in this group. The nor adrenaline content of corpus cavernosum from insulin dependent (IDDM) and non insulin dependent (NIDDM) diabetic neuropathic patients was also found to be significantly lower (P <0.02) than that of non diabetic non neuropathic patients. The nor adrenaline content of tunica albuginea however, was similar in both groups. A non significant association in the content of nor adrenaline in corpus cavernosum and tunica albuginea among IDDM and NIDDM diabetic neuropathics was also observed. These results provide evidence that an underlying neuropathic factor itself causes vascular as well as metabolic changes in the adrenergic nerves of the penis in diabetics due to neuropathy in addition to the effect of the disease and thus may contribute to the development of impotence in these patients irrespective of their type of diabetes.
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Affiliation(s)
- S T Ali
- Department of Physiology, University of Karachi, Karachi-75270, Pakistan
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Ali ST, Duncan AM, Schappert K, Heng HH, Tsui LC, Chow W, Robinson BH. Chromosomal localization of the human gene encoding the 51-kDa subunit of mitochondrial complex I (NDUFV1) to 11q13. Genomics 1993; 18:435-9. [PMID: 8288251 DOI: 10.1006/geno.1993.1493] [Citation(s) in RCA: 24] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The 51-kDa flavoprotein subunit of mitochondrial NADH:ubiquinone oxidoreductase (Complex I) [NADH dehydrogenase (ubiquinone), flavoprotein 1 (51 kDa); EC 1.6.5.3] plays an important role in the formation of the NADH-binding site and is believed to be the principal site of entry for electrons donated by NADH into the respiratory chain. Human cDNA fragments of the 51-kDa protein were generated by polymerase chain reaction and used to localize the gene (NDUFV1) for this subunit to 11q13 by two separate techniques. This region of the human genome is strongly implicated in a number of different forms of cancer.
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Affiliation(s)
- S T Ali
- Department of Genetics, University of Toronto, Ontario, Canada
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49
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Ali ST, Shaikh RN, Siddiqi NA, Siddiqi PQ. Comparative studies of the induction of erectile response to film and fantasy in diabetic men with and without neuropathy. Arch Androl 1993; 30:137-45. [PMID: 8498864 DOI: 10.3109/01485019308987747] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The present study deals with the diabetic neuropathies prevailing in men. In this investigation 100 insulin-dependent diabetes mellitus (IDDM) and 314 non-insulin-dependent diabetes mellitus (NIDDM) patients with and without an objective evidence of neuropathy, having an age span in between 15 and 60 years and a duration of diabetes distributed over 1-33 years, were included along with their age-matched nondiabetic controls. The diabetic subjects were evaluated for the induction of erectile responses. Investigation of induction of erectile responses to erotic stimulation by film and fantasy revealed striking results in diabetic patients with established neuropathy. Both IDDM and NIDDM patients with neuropathy exhibited a highly significant decrease (P < .0005) in penile diameter and length, penile arterial pulse amplitude, both systolic and diastolic blood pressures, and heart rate compared to controls of the same age group. However, both IDDM and NIDDM patients without neuropathy showed a nonsignificant difference in the above-mentioned parameters compared to control subjects. A nonsignificant association of induction of erectile responses to erotic stimulations among IDDM and NIDDM patients with and without neuropathy was also observed, suggesting that impotence and altered erectile responses are likely to be associated with an increased frequency to autonomic neuropathy in these patients irrespective of their type of diabetes.
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Affiliation(s)
- S T Ali
- Department of Physiology, University of Karachi, Pakistan
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50
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Ali ST, Shaikh RN, Ashfaqsiddiqi N, Siddiqi PQ. Serum and urinary levels of pituitary--gonadal hormones in insulin-dependent and non-insulin-dependent diabetic males with and without neuropathy. Arch Androl 1993; 30:117-23. [PMID: 8470941 DOI: 10.3109/01485019308987744] [Citation(s) in RCA: 21] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Diabetic neuropathies were studied in 100 insulin-dependent diabetes mellitus patients, 314 non-insulin-dependent diabetes mellitus patients with and without an objective evidence of neuropathy (age span, 15-80 years; duration of diabetes distributed over 1-33 years), and their age-matched nondiabetic controls. Serum and urinary levels of pituitary-gonadal hormones were evaluated in the diabetic subjects. There were striking results, i.e., a significantly low serum total and serum free (urinary) testosterone level (p < .0005) and a significantly high serum and urinary FSH and LH and serum prolactin level (p < .0005), specifically in the neuropathic diabetic patients, suggesting a series of pathological reactions in the smooth musculature of genital organs characterized by an increase in the interstitial thickness of seminiferous tubules, peritubular and intertubular fibrosis, and tubular sclerosis. Testicular necrosis, probably due to neuropathy, provided an additional aid to confirm these findings. A decrease in semen volume and sperm motility in the diabetic neuropathic patients further suggested the involvement of the entire smooth musculature of the reproductive tract, leading to atonia of the bladder and urethra. Such complications are purely neurogenic. The low serum and urinary testosterone levels and increased serum and urinary FSH and LH and serum prolactin levels in the diabetic men with neuropathy suggest gonadal disorder (hypogonadotropic hypogonadism), which may be due to testicular necrosis and thickening of seminiferous tubules, causing autonomic lesion.
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MESH Headings
- Adolescent
- Adult
- Aged
- Aged, 80 and over
- Diabetes Mellitus, Type 1/blood
- Diabetes Mellitus, Type 1/metabolism
- Diabetes Mellitus, Type 1/urine
- Diabetes Mellitus, Type 2/blood
- Diabetes Mellitus, Type 2/metabolism
- Diabetes Mellitus, Type 2/urine
- Diabetic Neuropathies/blood
- Diabetic Neuropathies/metabolism
- Diabetic Neuropathies/urine
- Follicle Stimulating Hormone/metabolism
- Gonadotropins, Pituitary/blood
- Gonadotropins, Pituitary/metabolism
- Gonadotropins, Pituitary/urine
- Humans
- Luteinizing Hormone/metabolism
- Male
- Middle Aged
- Prolactin/metabolism
- Testosterone/metabolism
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Affiliation(s)
- S T Ali
- Department of Physiology, University of Karachi, Pakistan
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