Health Metrics and the Spread of Infectious Diseases: Machine Learning Applications and Spatial Modelling Analysis with R is an introductory guide to health metrics and infectious diseases. It demonstrates how to calculate these metrics to compare the health status of different countries and explores the world of infectious diseases. It tests various machine learning tools for analyzing trends and relationships among key variables, aiming to prevent unexpected outcomes. Through detailed explanations and practical examples, readers will gain a comprehensive understanding of Disability Adjusted Life Years (DALYs) and their components.

Key Features:

• Structured into four main sections-foundational health metrics, machine learning applications, data visualization, and real-world case studies
• Integrates real-world case studies with data visualization and machine learning techniques, including spatial modelling with the R programming language
• Covers specific infectious diseases such as COVID-19 and malaria, providing insights into their spread and control
• Includes detailed explanations, practical exercises, and clear illustrations to enhance understanding and application
• Adopts a practical approach, making advanced concepts accessible to a wide audience The book is primarily aimed at researchers, data scientists, and public health professionals who seek to leverage data to improve health outcomes. By blending theoretical knowledge with practical applications, the book equips readers with the tools to make informed decisions and produce meaningful data analyses in public health.

Autorentext

Federica Gazzelloni is an Actuary and Statistician with a focus on health metrics, machine learning, and data visualisation. Her collaboration with the Institute for Health Metrics and Evaluation (IHME) inspired her to create this book as a practical guide for analysing health metrics data, bridging complex methodologies with real-world applications.

Prior to her work in public health, she gained experience in both corporate and academic settings, where she served as a research-oriented actuary, taught mathematics to high school students, and instructed university students in computer science. This varied background enables her to bridge complex statistical concepts with real-world applications, making data-driven insights accessible to broader audiences.

A dedicated advocate for open-source technology and inclusivity, Federica is an active contributor to organisations such as the Data Science Learning Community (DSLC), Actex Learning, The Carpentries, Bioconductor, and the R Consortium. As the lead organiser of R-Ladies Rome, she fosters a supportive space for underrepresented groups in technology to develop skills in data science and visualisation. Her passion for turning complex data into clear, actionable visual narratives is reflected throughout her work.

For updates on her latest projects and initiatives, visit federicagazzelloni.com.