Long short-term memory model for COVID-19 forecasting

Abstract

Every time new variants of COVID-19, back to the pandemic, could bring massive loss to human society and traditional models have failed to catch the complexities of COVID-19. Thus, to handle the unpredictable challenges posed by the dynamic nature of COVID-19, Long Short-Term Memory (LSTM) models were utilized to make accurate short and long-term predictions about different variants and prepare the model for another variant or virus similar to COVID-19 by learning the data of different COVID-19 variants. The dataset, sourced from owid-covid-data, is cleaned and divided into original, alpha, delta, and mixed variants in the United States from March 1, 2020, to April 30, 2022. MSE, RMSE, MAE, and R2 are used to compare the difference between real and predicted values to evaluate how accurately the model performs. Results demonstrate the model excels in long-term and short-term predicting COVID-19 cases and deaths for various variants, and mixed variants even promote accuracy. Thus, the proposed LSTM model shows promise for infectious disease forecasting, providing a foundation for anticipating future outbreaks to help policymakers make better decisions or early prevention.