Related Works

Statistical and Machine Learning Models

Many statistical methods including Susceptible Infectious Recovery and Auto-Regressive Integrated Moving Acerage model have been used to sumulate and forecast COVID-19 spread, yet they fall short of dealing with high-dimensional and temporal data. While they are easier to interpret comparing to Deep Learning Models due to DL’s black-box nature, deep learning models excel them in terms of performance accuracy and the ability to captue non-linear complex relationships across variables.

Deep Learing Models

Previously LSTM and Bi-LSTM models have demonstarted their remarkable performance because of RNN’s ability to learn from sequential data. Later on the Variational Auto Encoder has outperfoms RNN-based model. However, desptite Deep Learning Model’s outstanding performance, there are concerns about interpretability and their inability to capture socioeconomical factors.