A Dual Long Short-Term Memory Model in Forecasting the Number of COVID-19 Infections

Since the outbreak of Coronavirus Disease 2019 (COVID-19), spread epidemic has been a major international public health issue. Hence, various forecasting models have used to predict infectious disease. In general, problems often involve prediction accuracy decreasing as horizon increases. Thus, extend without performance or prediction, this study developed Dual Long Short-Term Memory (LSTM) with Genetic Algorithms (DULSTMGA) model. The model employed predicted values generated by LSTM in short-forecasting horizons inputs for long-term rolling manner. algorithms were applied determine parameters models, allowing increase long short-term was accurate. addition, compartment utilized simulate state COVID-19 and generate numbers cases. Infectious cases three countries examine feasibility proposed DULSTMGA Numerical results indicated that could obtain satisfactory superior many previous studies terms mean absolute percentage error. Therefore, designed is feasible promising alternative number