Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case
DC Field | Value | Language |
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dc.contributor.author | Rahmadani, Firda | - |
dc.contributor.author | Lee, Hyunsoo | - |
dc.date.available | 2021-01-22T06:40:13Z | - |
dc.date.created | 2021-01-22 | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/18554 | - |
dc.description.abstract | Featured Application The proposed framework is the hybrid deep learning framework using the meta-population model and LSTM. It is expected to contribute to the effective control of COVID-19 infection. The emergence of COVID-19 and the pandemic have changed and devastated every aspect of our lives. Before effective vaccines are widely used, it is important to predict the epidemic patterns of COVID-19. As SARS-CoV-2 is transferred primarily by droplets of infected people, the incorporation of human mobility is crucial in epidemic dynamics models. This study expands the susceptible-exposed-infected-recovered compartment model by considering human mobility among a number of regions. Although the expanded meta-population epidemic model exhibits better performance than general compartment models, it requires a more accurate estimation of the extended modeling parameters. To estimate the parameters of these epidemic models, the meta-population model is incorporated with deep learning models. The combined deep learning model generates more accurate modeling parameters, which are used for epidemic meta-population modeling. In order to demonstrate the effectiveness of the proposed hybrid deep learning framework, COVID-19 data in South Korea were tested, and the forecast of the epidemic patterns was compared with other estimation methods. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Rahmadani, Firda | - |
dc.contributor.affiliatedAuthor | Lee, Hyunsoo | - |
dc.identifier.doi | 10.3390/app10238539 | - |
dc.identifier.wosid | 000597737300001 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.10, no.23 | - |
dc.relation.isPartOf | APPLIED SCIENCES-BASEL | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 10 | - |
dc.citation.number | 23 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | epidemic modeling | - |
dc.subject.keywordAuthor | hybrid deep learning | - |
dc.subject.keywordAuthor | meta-population model | - |
dc.subject.keywordAuthor | human mobility | - |
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