Prediction of COVID-19 Spreading using LSTM Recurrent Neural Networks in Southeast Asia

Abstract

The world has come to a standstill due to the coronavirus. The number of confirmed cases and the number of deaths are increasing day by day. Now accurate and real-time predictions are very important. Currently, a lot of work is being done on the spread prediction of coronavirus. If we can predict the number of corona patients in a country in advance, then it will be benefcial for the government of that country to take action in advance. In this paper, we experiment with Bangladesh and India data using the deep learning method. We have used LSTM (LONG SHORT-TERM MEMORY) neural network which is a type of recurrent neural network. The LSTM method can work very well even with very little data. Since we do not currently have much data on covid19, LSTM neural networks may be a suitable model. We have got very good prediction results using this method even with very little data. The prediction of various parameters (number of confirmed cases per day, number of deaths per day, number of total confirmed cases, and number of total deaths). We also calculate MAPE, MAE, and RMSE values and compare them among different models. Our model outperforms others’ models. We think this research will be a beneficial tool for administrators and health officials.