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Systematic review and meta-analyses of superspreading of SARS-CoV-2 infections.
Du, Zhanwei; Wang, Chunyu; Liu, Caifen; Bai, Yuan; Pei, Sen; Adam, Dillon C; Wang, Lin; Wu, Peng; Lau, Eric H Y; Cowling, Benjamin J.
  • Du Z; Li Ka Shing Faculty of Medicine, WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Wang C; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
  • Liu C; Li Ka Shing Faculty of Medicine, WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Bai Y; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
  • Pei S; Li Ka Shing Faculty of Medicine, WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Adam DC; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
  • Wang L; Li Ka Shing Faculty of Medicine, WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Wu P; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
  • Lau EHY; Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York.
  • Cowling BJ; Li Ka Shing Faculty of Medicine, WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Transbound Emerg Dis ; 69(5): e3007-e3014, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1923067
ABSTRACT
Superspreading, or overdispersion in transmission, is a feature of SARS-CoV-2 transmission which results in surging epidemics and large clusters of infection. The dispersion parameter is a statistical parameter used to characterize and quantify heterogeneity. In the context of measuring transmissibility, it is analogous to measures of superspreading potential among populations by assuming that collective offspring distribution follows a negative-binomial distribution. We conducted a systematic review and meta-analysis on globally reported dispersion parameters of SARS-CoV-2 infection. All searches were carried out on 10 September 2021 in PubMed for articles published from 1 January 2020 to 10 September 2021. Multiple estimates of the dispersion parameter have been published for 17 studies, which could be related to where and when the data were obtained, in 8 countries (e.g. China, the United States, India, Indonesia, Israel, Japan, New Zealand and Singapore). High heterogeneity was reported among the included studies. The mean estimates of dispersion parameters range from 0.06 to 2.97 over eight countries, the pooled estimate was 0.55 (95% CI 0.30, 0.79), with changing means over countries and decreasing slightly with the increasing reproduction number. The expected proportion of cases accounting for 80% of all transmissions is 19% (95% CrI 7, 34) globally. The study location and method were found to be important drivers for diversity in estimates of dispersion parameters. While under high potential of superspreading, larger outbreaks could still occur with the import of the COVID-19 virus by traveling even when an epidemic seems to be under control.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics / COVID-19 Type of study: Observational study / Reviews / Systematic review/Meta Analysis Limits: Animals Country/Region as subject: Asia Language: English Journal: Transbound Emerg Dis Journal subject: Veterinary Medicine Year: 2022 Document Type: Article Affiliation country: Tbed.14655

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics / COVID-19 Type of study: Observational study / Reviews / Systematic review/Meta Analysis Limits: Animals Country/Region as subject: Asia Language: English Journal: Transbound Emerg Dis Journal subject: Veterinary Medicine Year: 2022 Document Type: Article Affiliation country: Tbed.14655