Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/87956
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | - |
dc.contributor | School of Nursing | - |
dc.creator | Zhao, S | - |
dc.date.accessioned | 2020-09-04T00:53:12Z | - |
dc.date.available | 2020-09-04T00:53:12Z | - |
dc.identifier.issn | 1547-1063 | - |
dc.identifier.uri | http://hdl.handle.net/10397/87956 | - |
dc.language.iso | en | en_US |
dc.publisher | American Institute of Mathematical Sciences | en_US |
dc.rights | © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0) | en_US |
dc.rights | The following publication Shi Zhao. Estimating the time interval between transmission generations when negative values occur in the serial interval data: using COVID-19 as an example. Mathematical Biosciences and Engineering, 2020, 17(4): 3512-3519, is available at https://doi.org/10.3934/mbe.2020198 | en_US |
dc.subject | Coronavirus disease 2019 | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | Epidemic | en_US |
dc.subject | Modelling | en_US |
dc.subject | Serial interval | en_US |
dc.subject | Time of generation | en_US |
dc.title | Estimating the time interval between transmission generations when negative values occur in the serial interval data : using COVID-19 as an example | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 3512 | - |
dc.identifier.epage | 3519 | - |
dc.identifier.volume | 17 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.3934/MBE.2020198 | - |
dcterms.abstract | The coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in the end of 2019, and soon became a serious public health threat globally. Due to the unobservability, the time interval between transmission generations (TG), though important for understanding the disease transmission patterns, of COVID-19 cannot be directly summarized from surveillance data. In this study, we develop a likelihood framework to estimate the TG and the pre-symptomatic transmission period from the serial interval observations from the individual transmission events. As the results, we estimate the mean of TG at 4.0 days (95%CI: 3.3−4.6), and the mean of pre-symptomatic transmission period at 2.2 days (95%CI: 1.3−4.7). We approximate the mean latent period of 3.3 days, and 32.2% (95%CI: 10.3−73.7) of the secondary infections may be due to pre-symptomatic transmission. The timely and effectively isolation of symptomatic COVID-19 cases is crucial for mitigating the epidemics. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Mathematical biosciences and engineering, 2020, v. 17, no. 4, p.3512-3519 | - |
dcterms.isPartOf | Mathematical biosciences and engineering | - |
dcterms.issued | 2020 | - |
dc.identifier.scopus | 2-s2.0-85086800319 | - |
dc.description.validate | 202009 bcma | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Zhao_Estimating_time_interval.pdf | 531.08 kB | Adobe PDF | View/Open |
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