Open AccessHybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case
Author(s) -
Firda Rahmadani,
Hyun Soo Lee
Publication year - 2020
Publication title -
applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.435
H-Index - 52
ISSN - 2076-3417
DOI - 10.3390/app10238539
Subject(s) - covid-19 , epidemic model , computer science , population , pandemic , deep learning , estimation , artificial intelligence , geography , virology , biology , infectious disease (medical specialty) , engineering , outbreak , medicine , environmental health , disease , systems engineering , pathology
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.