COVID-19 Forecasting Using Recurrent Neural Network and Machine Learning

Abstract

The COVID-19 variant’s complexity and the dire consequences of the variant spread are the inspirations behind the profound research on modeling and predicting new emerging variant surges. Multiple factors, including the variant characteristics, vaccination rate, the immune response of vaccinated individuals, and disease prevention health policies, impact the COVID-19 infection trend. The advancements in machine learning and neural network models, combined with the growth in computing, have demonstrated outstanding potential in modeling and predicting epidemic diseases. This research presents the modeling and prediction of the combined COVID-19 variant infection trends using the Holt-Winters exponential smoothing and seasonal auto-regressive integrated moving average with exogenous factors (SARIMAX) time-series machine learning models and recurrent neural network (RNN) long short-term memory (LSTM) model. Real-world United States COVID variant data from the Centers for Disease Control and Prevention (CDC) is used for prediction. The SARIMAX model factoring the seasonality of COVID-19 infections showed higher prediction accuracy than the Holt-Winters model, which is heavily weighted towards the most recent trends. The LSTM model had the best prediction accuracy of 91% with the lowest root mean square error (RMSE) values due to its property of selectively remembering patterns for long duration and the forget gates that correct the vanishing gradient problem, minimizing the error losses. This research demonstrates the promising application of the neural network deep learning models for epidemic disease modeling and prediction, enabling timely assessment of different policy decisions to mitigate the impact of an epidemic.