Enhancing COVID-19 Forecasting Accuracy in Malaysia Using a Hybrid ARIMA-LSTM Model With Exogenous Variables: A Time-Series Predictive Study.

Background: Accurate forecasting of COVID-19 cases is essential for effective public health planning and resource allocation. Traditional statistical and deep-learning models often fail to jointly capture linear dynamics, nonlinear patterns, and exogenous drivers of disease transmission. This study proposes a hybrid ARIMA-LSTM forecasting framework incorporating four exogenous variables-daily average temperature, rainfall, vaccination rate, and population density-at both the linear (ARIMAX) and nonlinear (LSTM residual) stages. Methods: Daily confirmed COVID-19 cases in Malaysia from January 4 to September 18, 2021 were analyzed. A dual-integration modeling strategy was implemented: an ARIMAX component modeled linear trends and exogenous effects (temperature, rainfall, vaccination rate, and population density), while a Long Short-Term Memory (LSTM) network captured nonlinear residual structures. Four competing models were evaluated: standalone ARIMA, standalone LSTM, hybrid ARIMA-LSTM without exogenous variables, and the proposed hybrid ARIMAX-LSTM with exogenous variables. Performance was assessed using RMSE, MAE, MAPE, and Results: The proposed hybrid ARIMAX-LSTM model achieved superior predictive accuracy (RMSE = 948.62; MAE = 769.49; MAPE = 6.61%; Conclusions: Integrating exogenous variables within both linear and nonlinear components substantially enhances COVID-19 forecasting accuracy. The proposed hybrid ARIMAX-LSTM framework provides a reliable tool for epidemic prediction and supports evidence-based public health decision-making. This approach is readily may be extensible to other infectious diseases and time-series forecasting applications.