Hybrid Deep Learning Model: LSTM and 2BiGRU for Predicting Coronavirus (COVID-19)

Abstract

The COVID-19 pandemic has had a major global health impact, highlighting the urgent need for accurate predictive models to forecast the virus's spread. This research explores the use of deep learning techniques to improve the accuracy of COVID-19 case predictions. Traditional machine learning methods often struggle with the complexities of time-series data inherent in pandemic forecasting, which motivates the use of advanced deep learning models. This study employs the LSTM-2BiGRU model, a sophisticated deep learning architecture, to predict new COVID-19 cases using two datasets: historical data from OurWorldInData and medical data with historical disease records. The model was trained to leverage time-dependent factors and achieve high prediction performance. The LSTM-2BiGRU model achieved a significant improvement over traditional machine learning models, with an accuracy of 76% and a Mean Absolute Error (MAE) of 8% for the historical dataset within a 7-day forecast window. When applied to the epidemiology dataset, the model demonstrated even higher accuracy, ranging from 80% to 90% across different prediction periods (1 to 14 days), with a Mean Absolute Percentage Error (MAPE) between 10% and 15%. These findings demonstrate the potential of deep learning models like LSTM-2BiGRU to provide more accurate and timely forecasts for COVID-19, with a substantial reduction in Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) compared to previous studies. This underscores the model's improved performance and supports better-informed public health decisions.