The COVID-19 pandemic has presented unprecedented challenges to global healthcare systems, underscoring the critical need for accurate prediction of infection cases to facilitate effective resource allocation and decision-making. This study evaluates the performance of two widely used time-series forecasting models, ARIMA and LSTM in predicting COVID-19 infection trends. Using a dataset of daily infection cases spanning January 2020 to June 2020, both models were trained and evaluated. The results demonstrate that the LSTM model achieves superior performance compared to the ARIMA model, as evidenced by lower Mean Absolute Error (MAE) and Mean Squared Error (MSE). The LSTM models ability to capture complex patterns and non-linear relationships in the data contributes significantly to its enhanced predictive accuracy. These findings highlight the potential of LSTM models to deliver more reliable forecasts of COVID-19 infection cases, providing healthcare authorities with valuable insights to inform strategic planning and preparedness for future outbreaks.
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