Preface

Welcome to The Public Health AI Handbook.

This handbook is designed for epidemiologists, public health practitioners, students, and policymakers who want to understand and apply artificial intelligence in their work—without becoming machine learning engineers.

Who This Book Is For

Public health practitioners seeking to understand AI capabilities and limitations
Epidemiologists who want practical AI tools for surveillance, forecasting, and analysis
Students entering the intersection of public health and data science
Policymakers making decisions about AI adoption in health systems

How to Use This Book

This handbook is organized into five parts:

Foundations: Essential AI concepts and data principles
Current Applications: Real-world AI uses in public health today
Implementation: Evaluation, ethics, and deployment
Practical Resources: Tools, code, and getting started guides
The Future: Emerging technologies and where the field is heading

Each chapter includes:

Core concepts explained in plain language
Runnable code examples
Real case studies
Reality checks on limitations
Further resources

What Makes This Different

Practical-first: Real tools and working code, not just theory
Honest about limitations: We highlight failures as much as successes
Open and evolving: Hosted on GitHub, community contributions welcome
Domain-focused: Built by public health professionals, for public health professionals

Contributing

This is a living handbook. Found an error? Have a case study to share? Want to improve a chapter?

Visit our GitHub repository to contribute.

Acknowledgments

This handbook builds on the work of countless researchers, practitioners, and educators. Special inspiration from The Epidemiologist’s R Handbook and the open science community.

License: MIT How to cite this handbook:

Tegomoh, B. (2025). The Public Health AI Handbook: A Practical Guide for Epidemiologists and Public Health Practitioners. https://publichealthaihandbook.com