Machine Learning Approaches for Epidemic Prediction: A Comprehensive Review

Abstract Epidemics have in the past caused severe public health challenges, calling for precise and timely prediction models to help limit their effects. Conventional statistical approaches tend to fall short of representing the intricacies of epidemic dynamics, thus calling for machine learning (ML) methods. With the large datasets from multiple sources, ML models are capable of detecting patterns and forecasting epidemic trends with improved accuracy. This review examines the application of ML in predicting epidemics, covering different algorithms, data, and model evaluation methods. Machine learning methods like supervised learning, unsupervised learning, and deep learning have proved to be highly successful in predicting disease outbreaks. Supervised models like decision trees, support vector machines, and neural networks are commonly applied for classification and time-series prediction. Unsupervised techniques like clustering and principal component analysis help in revealing underlying correlations in epidemiological data. Deep learning algorithms, specifically recurrent neural networks (RNNs) and transformers, provide sophisticated prediction functions by processing sequential data and providing meaningful insights from large-scale datasets. Notwithstanding the potential of ML in epidemic prediction, a number of challenges exist, such as data quality concerns, explainability of intricate models, and the need for extensive computations. The review showcases present developments in ML-based epidemic forecasting while solving the above challenges and suggesting directions for future studies. By combining ML with forthcoming technologies like IoT, cloud computing, and explainable AI, epidemic prediction can become more accurate and actionable, and eventually, better public health readiness and response. Keywords: Machine Learning, Epidemic Prediction, Supervised Learning, Unsupervised Learning, Deep Learning, Public Health.