The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Chaeyoung | - |
dc.contributor.author | Li, Yibao | - |
dc.contributor.author | Kim, Junseok | - |
dc.date.accessioned | 2021-08-30T13:23:20Z | - |
dc.date.available | 2021-08-30T13:23:20Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 0960-0779 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/52689 | - |
dc.description.abstract | In this article, we propose the Susceptible-Unidentified infected-Confirmed (SUC) epidemic model for estimating the unidentified infected population for coronavirus disease 2019 (COVID-19) in China. The unidentified infected population means the infected but not identified people. They are not yet hospitalized and still can spread the disease to the susceptible. To estimate the unidentified infected population, we find the optimal model parameters which best fit the confirmed case data in the least-squares sense. Here, we use the time series data of the confirmed cases in China reported by World Health Organization. In addition, we perform the practical identifiability analysis of the proposed model using the Monte Carlo simulation. The proposed model is simple but potentially useful in estimating the unidentified infected population to monitor the effectiveness of interventions and to prepare the quantity of protective masks or COVID-19 diagnostic kit to supply, hospital beds, medical staffs, and so on. Therefore, to control the spread of the infectious disease, it is essential to estimate the number of the unidentified infected population. The proposed SUC model can be used as a basic building block mathematical equation for estimating unidentified infected population. (C) 2020 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | IDENTIFIABILITY | - |
dc.subject | VACCINATION | - |
dc.title | The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19 | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Junseok | - |
dc.identifier.doi | 10.1016/j.chaos.2020.110090 | - |
dc.identifier.scopusid | 2-s2.0-85087653950 | - |
dc.identifier.wosid | 000588433800090 | - |
dc.identifier.bibliographicCitation | CHAOS SOLITONS & FRACTALS, v.139 | - |
dc.relation.isPartOf | CHAOS SOLITONS & FRACTALS | - |
dc.citation.title | CHAOS SOLITONS & FRACTALS | - |
dc.citation.volume | 139 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Physics, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Mathematical | - |
dc.subject.keywordPlus | IDENTIFIABILITY | - |
dc.subject.keywordPlus | VACCINATION | - |
dc.subject.keywordAuthor | Epidemic model | - |
dc.subject.keywordAuthor | Least-squares fitting | - |
dc.subject.keywordAuthor | COVID-19 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.