Modeling the Spread of an Epidemic Using Artificial Intelligence

Abstract

The COVID-19 pandemic has demonstrated the critical importance of accurate and timely epidemic modeling to guide public health responses. Traditional compartmental models such as SIR and SEIR, while effective in capturing fundamental transmission dynamics, often rely on fixed parameters and assumptions that may not hold in complex, real-world scenarios. In contrast, artificial intelligence (AI) offers a data-driven alternative capable of learning from vast and evolving datasets. This study explores the application of AI techniques specifically deep learning models such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) to forecast the spread of COVID-19. These models are trained on time-series data including daily case counts, mobility trends, and government intervention measures. This study explores the application of artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) to model the spread of COVID-19. By leveraging AI-driven approaches such as recurrent neural networks (RNNs), long short-term memory (LSTM) models, and Bidirectional LSTM Model.