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Differences in the superspreading potentials of COVID-19 across contact settings.
Zhao, Yanji; Zhao, Shi; Guo, Zihao; Yuan, Ziyue; Ran, Jinjun; Wu, Lan; Yu, Lin; Li, Hujiaojiao; Shi, Yu; He, Daihai.
  • Zhao Y; Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
  • Zhao S; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China. zhaoshi.cmsa@gmail.com.
  • Guo Z; CUHK Shenzhen Research Institute, Shenzhen, China. zhaoshi.cmsa@gmail.com.
  • Yuan Z; Centre for Health Systems and Policy Research, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China. zhaoshi.cmsa@gmail.com.
  • Ran J; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
  • Wu L; Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China.
  • Yu L; School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Li H; Department of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China.
  • Shi Y; Faculty of Arts and Sciences, University of Toronto, Toronto, Canada.
  • He D; Faculty of Arts and Sciences, University of Toronto, Toronto, Canada.
BMC Infect Dis ; 22(1): 936, 2022 Dec 12.
Article in English | MEDLINE | ID: covidwho-2162314
ABSTRACT

BACKGROUND:

Superspreading events (SSEs) played a critical role in fueling the COVID-19 outbreaks. Although it is well-known that COVID-19 epidemics exhibited substantial superspreading potential, little is known about the risk of observing SSEs in different contact settings. In this study, we aimed to assess the potential of superspreading in different contact settings in Japan.

METHOD:

Transmission cluster data from Japan was collected between January and July 2020. Infector-infectee transmission pairs were constructed based on the contact tracing history. We fitted the data to negative binomial models to estimate the effective reproduction number (R) and dispersion parameter (k). Other epidemiological issues relating to the superspreading potential were also calculated.

RESULTS:

The overall estimated R and k are 0.561 (95% CrI 0.496, 0.640) and 0.221 (95% CrI 0.186, 0.262), respectively. The transmission in community, healthcare facilities and school manifest relatively higher superspreading potentials, compared to other contact settings. We inferred that 13.14% (95% CrI 11.55%, 14.87%) of the most infectious cases generated 80% of the total transmission events. The probabilities of observing superspreading events for entire population and community, household, health care facilities, school, workplace contact settings are 1.75% (95% CrI 1.57%, 1.99%), 0.49% (95% CrI 0.22%, 1.18%), 0.07% (95% CrI 0.06%, 0.08%), 0.67% (95% CrI 0.31%, 1.21%), 0.33% (95% CrI 0.13%, 0.94%), 0.32% (95% CrI 0.21%, 0.60%), respectively.

CONCLUSION:

The different potentials of superspreading in contact settings highlighted the need to continuously monitoring the transmissibility accompanied with the dispersion parameter, to timely identify high risk settings favoring the occurrence of SSEs.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: BMC Infect Dis Journal subject: Communicable Diseases Year: 2022 Document Type: Article Affiliation country: S12879-022-07928-9

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: BMC Infect Dis Journal subject: Communicable Diseases Year: 2022 Document Type: Article Affiliation country: S12879-022-07928-9