Introduction to Machine Learning

Le résumé fourni par la source

This introductory chapter serves as a foundational guide to machine learning in the context of infectious disease modelling. It begins by distinguishing deterministic and stochastic models, emphasising their relevance in capturing both predictable and random aspects of disease spread. The chapter introduces machine learning concepts through practical examples and explores both basic techniques, such as linear regression, and more advanced methods, like empirically driven models (e.g., Random Forest and SIR models). Readers are guided through essential steps of model construction, including data preprocessing, parameter optimization, and model evaluation. Practical case studies, such as the modelling of cholera and hypothetical epidemics, illustrate the application of machine learning to dynamic health phenomena. The chapter also examines the impact of rapidly spreading diseases on local and global health metrics, providing insights into how individual country dynamics contribute to global health perspectives. By bridging theory and practice, this chapter equips readers with the knowledge to apply machine learning methodologies effectively to analyse and predict infectious disease dynamics.

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