Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/99602
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Applied Mathematics | - |
| dc.contributor | Department of Civil and Environmental Engineering | - |
| dc.contributor | Research Institute for Future Food | - |
| dc.creator | Zhao, Y | en_US |
| dc.creator | Zhao, S | en_US |
| dc.creator | Guo, Z | en_US |
| dc.creator | Yuan, Z | en_US |
| dc.creator | Ran, J | en_US |
| dc.creator | Wu, L | en_US |
| dc.creator | Yu, L | en_US |
| dc.creator | Li, H | en_US |
| dc.creator | Shi, Y | en_US |
| dc.creator | He, D | en_US |
| dc.date.accessioned | 2023-07-18T03:11:32Z | - |
| dc.date.available | 2023-07-18T03:11:32Z | - |
| dc.identifier.uri | http://hdl.handle.net/10397/99602 | - |
| dc.language.iso | en | en_US |
| dc.publisher | BioMed Central Ltd | en_US |
| dc.rights | © The Author(s) 2022. | en_US |
| dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | en_US |
| dc.rights | The following publication Zhao, Y., Zhao, S., Guo, Z. et al. Differences in the superspreading potentials of COVID-19 across contact settings. BMC Infect Dis 22, 936 (2022) is available at https://doi.org/10.1186/s12879-022-07928-9. | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Contact settings | en_US |
| dc.subject | Superspreading | en_US |
| dc.subject | Transmission heterogeneity | en_US |
| dc.title | Differences in the superspreading potentials of COVID-19 across contact settings | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 22 | en_US |
| dc.identifier.issue | 1 | en_US |
| dc.identifier.doi | 10.1186/s12879-022-07928-9 | en_US |
| dcterms.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. | - |
| dcterms.abstract | 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. | - |
| dcterms.abstract | 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. | - |
| dcterms.abstract | 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. | - |
| dcterms.accessRights | open access | en_US |
| dcterms.bibliographicCitation | BMC infectious diseases, 2022, v. 22, no. 1, 936 | en_US |
| dcterms.isPartOf | BMC infectious diseases | en_US |
| dcterms.issued | 2022 | - |
| dc.identifier.scopus | 2-s2.0-85143994082 | - |
| dc.identifier.pmid | 36510138 | - |
| dc.identifier.eissn | 1471-2334 | en_US |
| dc.identifier.artn | 936 | en_US |
| dc.description.validate | 202307 bcch | - |
| dc.description.oa | Version of Record | en_US |
| dc.identifier.FolderNumber | OA_Scopus/WOS | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.description.oaCategory | CC | en_US |
| Appears in Collections: | Journal/Magazine Article | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Zhao_Differences_Superspreading_Potentials.pdf | 935.77 kB | Adobe PDF | View/Open |
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