Time Series Forecasting of Epidemics Using LSTM Networks

Abstract

Accurate forecasting of epidemic outbreaks is essential for effective public health planning and timely intervention. This paper presents a Time Series Forecasting framework for Epidemics using Long Short-Term Memory (LSTM) networks to model temporal patterns in disease spread. The proposed system utilizes historical epidemiological data, including infection rates, recovery trends, and transmission dynamics, to predict future outbreak scenarios. LSTM networks are employed due to their ability to capture long-term dependencies and nonlinear relationships in sequential data. Data preprocessing techniques such as normalization and sliding window approaches are applied to improve model performance. The framework is further enhanced through hybrid approaches that integrate statistical methods with deep learning models for improved accuracy. Experimental results demonstrate that the proposed model provides reliable forecasts and outperforms traditional time series models in capturing complex epidemic trends. Additionally, integration with prior research in healthcare analytics strengthens the robustness and generalization capability of the system. The study highlights the potential of LSTM-based forecasting in supporting data-driven decision-making and effective epidemic management strategies.