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      The impact of mass gatherings on the local transmission of COVID-19 and the implications for social distancing policies: Evidence from Hong Kong

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          Abstract

          Mass gatherings provide conditions for the transmission of infectious diseases and pose complex challenges to public health. Faced with the COVID-19 pandemic, governments and health experts called for suspension of gatherings in order to reduce social contact via which virus is transmitted. However, few studies have investigated the contribution of mass gatherings to COVID-19 transmission in local communities. In Hong Kong, the coincidence of the relaxation of group gathering restrictions with demonstrations against the National Security Law in mid-2020 raised concerns about the safety of mass gatherings under the pandemic. Therefore, this study examines the impacts of mass gatherings on the local transmission of COVID-19 and evaluates the importance of social distancing policies. With an aggregated dataset of epidemiological, city-level meteorological and socioeconomic data, a Synthetic Control Method (SCM) is used for constructing a ‘synthetic Hong Kong’ from over 200 Chinese cities. This counterfactual control unit is used to simulate COVID-19 infection patterns (i.e., the number of total cases and daily new cases) in the absence of mass gatherings. Comparing the hypothetical trends and the actual ones, our results indicate that the infection rate observed in Hong Kong is substantially higher than that in the counterfactual control unit (2.63% vs. 0.07%). As estimated, mass gatherings increased the number of new infections by 62 cases (or 87.58% of total new cases) over the 10–day period and by 737 cases (or 97.23%) over the 30-day period. These findings suggest the necessity of tightening social distancing policies, especially the prohibition on group gathering regulation (POGGR), to prevent and control COVID-19 outbreaks.

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          Most cited references46

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          Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

          Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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            Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program

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              The Economic Costs of Conflict: A Case Study of the Basque Country

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                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: Writing – review & editing
                Role: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                1 February 2023
                2023
                1 February 2023
                : 18
                : 2
                : e0279539
                Affiliations
                [1 ] Urban Governance and Design Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Hong Kong
                [2 ] Hong Kong University of Science and Technology, Kowloon, Hong Kong
                [3 ] University of Glasgow, Glasgow, United Kingdom
                [4 ] International Institute for Applied Systems Analysis
                [5 ] University of Hong Kong, Pokfulam, Hong Kong
                Jahangirnagar University, BANGLADESH
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-0892-3606
                https://orcid.org/0000-0002-5548-8213
                https://orcid.org/0000-0001-6041-8191
                https://orcid.org/0000-0001-9633-8513
                Article
                PONE-D-21-30177
                10.1371/journal.pone.0279539
                9891527
                36724151
                af8a9108-2e16-4b53-a3e1-62128320e39c
                © 2023 Zhu et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 21 September 2021
                : 8 December 2022
                Page count
                Figures: 5, Tables: 3, Pages: 20
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                People and places
                Geographical locations
                Asia
                Hong Kong
                Earth Sciences
                Geography
                Human Geography
                Urban Geography
                Cities
                Social Sciences
                Human Geography
                Urban Geography
                Cities
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Infectious Disease Control
                Social Distancing
                Social Sciences
                Sociology
                Social Policy
                Medicine and Health Sciences
                Epidemiology
                Pandemics
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Medicine and Health Sciences
                Epidemiology
                Custom metadata
                The city-level COVID-19 data are available from the China Data Lab Dataverse ( https://dataverse.harvard.edu/dataverse/cdl_dataverse). The city-level demographic and socioeconomic data can be found from the 2019 China City Statistical Yearbook ( http://www.stats.gov.cn/tjsj/ndsj/2019/indexeh.htm). The natural meteorological data are available at the China Meteorological Data Service Centre ( http://data.cma.cn/en), the Hong Kong Observatory ( https://www.hko.gov.hk/en/index.html), and Macao Meteorological and Geophysical Bureau ( https://www.smg.gov.mo/en).
                COVID-19

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