# Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case
## 5 Conclusions  
The epidemic prediction of infectious diseases is one of the most widely studied research areas. A number of research studies using time-series analysis, regression studies, and other statistical methods have been conducted to explain epidemic dynamics. In addition, deep learning approaches have been introduced for forecasting. These methods have been applied for tracking COVID-19 patterns. The emergence of the COVID-19 virus has changed every aspect of our lives and has devastated all societies. Before the effective vaccines come into wide use, it is important to accurately predict the epidemic patterns of COVID-19. However, it is difficult to estimate the pattern owing to the characteristics of COVID-19. As infection is primarily mediated by the droplets of infected people, human mobility has to be considered and incorporated in epidemic analysis. As COVID-19 follows an SEIR-based infection model, the proposed framework implements an SEIR compartment model. In order to incorporate human mobility among regions, the SEIR compartment model is expanded with relevant human mobility-based modeling parameters and terms. Although the expanded epidemic model has advantages over simple compartment models, its performance depends on the accurate estimation of the epidemic model parameters. To increase the accuracy of parameter estimation, deep learning approaches are deployed. The proposed hybrid deep learning framework consists of an epidemic meta-model and a deep learning model. The estimated epidemic status and real COVID-19 historical data are input to the deep learning model, and the parameters of the meta-population model are generated as output. The weight of the deep learning model is updated with the minimization of the gap between the real COVID-19 status and the estimated status computed from the meta-population model. To demonstrate the effectiveness of the proposed hybrid deep learning model, the estimation results are compared with other forecasting methods. The numerical analysis demonstrates that the hybrid deep learning framework using the meta-population model and LSTM model exhibits the best performance among the testing methods. Therefore, the proposed framework is expected to contribute to the effective control of COVID-19 infection. In future studies, the epidemic patterns of various infectious diseases can be predicted using the proposed hybrid deep learning framework. As the spread of infectious disease follows a specific epidemic pattern, the meta-population model of the proposed hybrid deep learning framework can be modified and applied depending on the disease scenario. In addition, the deep learning model used can be replaced with more effective models. This research focuses on the prediction of COVID-19 spread considering human mobility. While a number of research studies propose effective restrictions on human mobility, the proposed framework can help the prevention and control of the COVID-19 spread. [PERSON] and [PERSON] conceptualized the framework and developed the methodologies. [PERSON] implemented and validated the framework. [PERSON] supervised the overall research processes. [PERSON] wrote the manuscript and [PERSON] reviewed and edited it. All authors have read and agreed to the published version of the manuscript. This research is funded by Professor Research Year System, Kumoh National Institute of Technology, S. Korea. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Comparison**} & \multicolumn{3}{c}{**Seoul Case**} & \multicolumn{3}{c}{**Daegu Case**} \\ \cline{2-6}  & & \(\mathbf{I_{t}}\) & \(\mathbf{R_{t}}\) & \(\mathbf{I_{t}}\) & \(\mathbf{R_{t}}\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} The proposed hybrid deep \\ learning framework \\ \end{tabular} } & Meta-population model + LSTM & 3.04 & 0.14 & 0.84 & 0.12 \\ \cline{2-6}  & Meta-population model + DNN & 9.20 & 0.19 & 4.57 & 0.14 \\ \hline LSTM & 5.70 & 0.17 & 1.05 & 0.20 \\ \hline DNN & 11.24 & 0.21 & 4.45 & 0.21 \\ \hline \hline \end{tabular}
\end{table}