Time-Series Forecasting of Meningitis Out Breaks using ARIMA and LSTM Models

Preventive healthcare planning and prompt medical response depend on the precise forecasting of meningitis outbreaks. In this study, two different time-series forecasting techniques—ARIMA, a traditional statistical method, and LSTM, a deep learning-based recurrent neural network—are used to analyze the temporal patterns of meningitis cases. Historical data on meningitis incidence is collected from credible sources, carefully preprocessed, and then used to train both models. ARIMA is employed to model the linear and seasonal components in the data, making it effective for capturing consistent trends over time. In contrast, LSTM is designed to learn complex, non-linear temporal dependencies and is particularly suited for datasets exhibiting irregular patterns and abrupt fluctuations. Standard performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used to evaluate the predictive accuracy of each model. The results reveal that while ARIMA provides dependable short-term forecasts with interpretable trends, LSTM demonstrates superior accuracy in capturing sudden outbreaks and long-term nonlinear shifts. Moreover, a hybrid model combining both ARIMA and LSTM is explored to leverage the strengths of each technique, resulting in improved overall forecasting performance. This comparative analysis highlights not only the technical capabilities of each method but also their practical relevance for real-world disease surveillance. The study underscores the need for hybrid frameworks that can handle both predictable seasonal trends and unpredictable epidemic surges. By enabling proactive resource allocation, early intervention strategies, and informed policymaking, this dual-model approach serves as a valuable decision-support tool for public health authorities in managing and mitigating the impact of meningitis outbreaks.