# Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case
## 2 Background and Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research Studies** & **COVID-19 Related Application** & **Characteristics** & **Issues** \\ \hline [PERSON] et al. [18] & -Policy-relevant analysis for COVID-19 spread control & - Measurement and forecasting of the impacts of social distancing. & - Usage of a number of assumptions to avoid overfitting \\  & -Used methods: Parsimonious models for early-time data & - Geographical consideration and segmentation & - Comparative error for long-term prediction \\ \hline [PERSON] et al. [19] & - Prediction of the positive case of COVID-19 - Used method: deep LSTM & - Geographical consideration and segmentation & - Comparative error for long-term prediction \\ \hline [PERSON] et al. [20] & - Prediction of the COVID-19 incidence in Iran - Used methods: linear regression and LSTM & - Usage of 10-fold cross-validation & - Occurrence of overfitting in LSTM model \\ \hline [PERSON] et al. [21] & - Chest CT scan-based COVID-19 detection - Used method: residual network & - Understanding of the radiographic manifestations & - Difficult detection in the early phase of COVID-19 \\ \hline [PERSON] et al. [22] & - Identification of potential high-risk COVID-19 patients & - Strategies with high and low risk patient groups & - Ignorance of human mobility \\ \hline  & - Used method: deep neural network & - Consideration of time-variant properties & - Ignorance of human mobility \\ \hline [PERSON] et al. [23] & - Prediction of the number of new and recovered cases & - Consideration of time-variant properties & - Ignorance of human mobility \\  & - Used methods: LSTM, bidirectional LSTM and variational autoencoder (VAE) & - Consideration of time-variant properties & - Ignorance of human mobility \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._As shown in Table 1, most of the COVID 19-related research studies ignored human mobility and its considerations owing to limitations of the frameworks used. [PERSON] and [PERSON] [24] and [PERSON] et al. [25] showed the relationship between the initial spread of COVID-19 and human mobility. [PERSON] et al. [26] showed that human mobility can explain up to 92% of the initial spreads in France and Italy. [PERSON] et al. [26] considered the potential effects of various intra-city mobility restrictions to prevent the transmission of the disease. The spread relationship with human mobility made a number of countries implement human mobility-based policies [27, 28] such as social distancing, stay-at-home policy, and several travel bans. [PERSON] et al. [29] and [PERSON] et al. [30] indicated a significant relationship between human mobility and restrictions on public transport. However, detailed prediction models considering human mobility have provided comparatively less owing to data issues and modeling complexities. To overcome this issue, this research incorporates a deep learning approach into a COVID-19 SEIR model considering human mobility. There are several relevant research studies using deep learning methods. Table 2 summarizes the characteristics of the relevant research studies using a deep learning approach. A number of research studies have applied deep learning approaches to detect epidemic patterns or predict future infectious features; the characteristics of these studies are summarized in Table 2. However, the integration of an ODE-based compartment framework in a deep learning framework has been studied comparatively so far. This research study combines both approaches and shows their effectiveness in predicting the epidemic trend of COVID-19.