Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case
- Authors
- Rahmadani, Firda; Lee, Hyunsoo
- Issue Date
- Dec-2020
- Publisher
- MDPI
- Keywords
- COVID-19; epidemic modeling; hybrid deep learning; meta-population model; human mobility
- Citation
- APPLIED SCIENCES-BASEL, v.10, no.23
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 10
- Number
- 23
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/18554
- DOI
- 10.3390/app10238539
- ISSN
- 2076-3417
- 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.
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Collections - School of Industrial Engineering > 1. Journal Articles
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