An investigation of multivariate data-driven deep learning models for predicting COVID-19 variants

Abstract

The Coronavirus Disease 2019 (COVID-19) pandemic has swept almost all parts of the world. With the increasing number of COVID-19 cases worldwide, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has mutated into various variants. Given the increasingly dangerous conditions of the pandemic, it is crucial to predict the number of COVID-19 cases. Deep Learning and Long Short-Term Memory (LSTM) have predicted disease progress with reasonable accuracy and small errors. LSTM training is used to predict confirmed cases of COVID-19 based on variants identified using the European Centre for Disease Prevention and Control (ECDC) COVID-19 dataset containing confirmed cases identified from 30 European countries. Tests were conducted using the LSTM and Bidirectional LSTM (BiLSTM) models with the addition of Recurrent Neural Network (RNN) as comparisons on hidden size and layer size. The obtained result showed that in testing hidden sizes 25, 50, 75, and 100, the RNN model provided better results, with the minimum Mean Squared Error (MSE) value of 0.01 and the Root Mean Square Error (RMSE) value of 0.012 for B.1.427/B.1.429 variant with a hidden size of 100. Further testing layer sizes 2, 3, 4, and 5 shows that the BiLSTM model provided better results, with a minimum MSE value of 0.01 and an RMSE of 0.01 for the B.1.427/B.1.429 variant with a hidden size of 100 and layer size of 2.