Algorithms Using Machine Learning to Forecast Disease Outbreaks

Abstract

The increasing frequency and severity of disease outbreaks have underscored the critical need for effective forecasting methods to enable timely responses and resource allocation. This paper explores the application of machine learning algorithms to forecast disease outbreaks by leveraging diverse datasets, including epidemiological records, climate data, social media trends, and population mobility patterns. Various supervised and unsupervised learning techniques, such as decision trees, support vector machines, random forests, and deep learning models, are examined for their predictive accuracy and adaptability across different diseases and geographical regions. The study also discusses key challenges, including data quality, model interpretability, and real-time prediction capabilities. Results from comparative experiments highlight that ensemble and neural network-based approaches outperform traditional statistical models in outbreak detection and trend forecasting. This research demonstrates the potential of machine learning to enhance early warning systems and supports the development of more proactive and data-driven public health strategies.