Preface

AI as a Tool, Not a Revolution

Public health has always been about translation. We convert complex science into actionable intervention. We transform data into decisions, connecting what we know to what we do.

Artificial intelligence is entering our field not as a revolution, but as another tool requiring translation.

The challenge isn’t whether AI will change public health practice. It already has. Outbreak detection algorithms scan billions of social media posts. Predictive models forecast disease spread across continents. Natural language processing extracts insights from unstructured clinical notes. Chatbots deliver health information at scale.

The real challenge is this: How do we, as public health professionals, evaluate these tools critically, deploy them responsibly, and understand their limitations honestly without needing computer science degrees?

This handbook attempts that translation.

What This Handbook Is Not

This is not a technical manual for building state-of-the-art AI systems. If you want to architect neural networks or optimize gradient descent algorithms, excellent resources exist elsewhere.

This is not a manifesto claiming AI will solve all public health challenges. It won’t. Many of our field’s hardest problems, health inequity, structural determinism, inadequate funding, political barriers to evidence-based policy, are fundamentally human problems that no algorithm can fix.

This is not a catalog of futuristic possibilities. I’ve tried to focus on what exists now, what works (sometimes), what fails (often), and what we can actually implement with real data, real constraints, and real public health infrastructure.

What This Handbook Attempts

I’ve tried to write the resource I needed three years ago when I started encountering AI applications in surveillance work and realized I didn’t have a framework to evaluate them.

This handbook attempts to:

  • Demystify without oversimplifying , AI isn’t magic, but it’s also not just “fancy statistics”
  • Show working examples, not just concepts , Every major technique includes code you can run and modify
  • Acknowledge failures as loudly as successes , Most AI projects fail. Learning why matters more than celebrating the rare successes
  • Ground everything in public health context , The technical details matter less than understanding when a tool is appropriate for your specific problem
  • Remain honest about uncertainty , I don’t have all the answers. The field is evolving faster than any handbook can track

A Note on Dual Audiences

This handbook serves two groups that often talk past each other.

Public health practitioners have seen this before. Electronic health records promised interoperability and delivered vendor lock-in. Google Flu Trends worked until it didn’t. Predictive policing tools encoded the biases in their training data. When someone says “AI will transform public health,” practitioners remember fax machines that still won’t die, surveillance systems held together with Excel macros, and vendors who disappeared after the contract was signed.

AI developers and data scientists see problems that look solvable. Why does it take years to validate tools that could save lives now? Why do health departments resist automation that works in other industries? Why is public health data so terrible?

Both groups are right about what frustrates them. AI tools do fail in deployment. Public health infrastructure does lag behind what’s technically possible. The gap between a working prototype and a tool that survives contact with a county health department is real.

This handbook holds both views. Some AI applications work. Many don’t. The chapters that follow try to distinguish between them based on actual evidence, not hope or hype.

Stay skeptical. Question the tools, the hype, the limitations I’ve described, and even the frameworks I’ve proposed. Public health has suffered before from adopting technologies uncritically.

How to Use the Chapter Summaries (TL;DRs)

You don’t need to read sequentially. Jump to whatever section addresses your immediate problem. Use the search function. Read the TL;DR summaries for quick orientation. Dive deep into the code when implementing something.

Three approaches:

  1. Quick Scan: Read chapter TL;DRs only for rapid orientation
  2. Deep Dive: Full chapters for implementation planning and code examples
  3. Reference Lookup: Search for specific questions, read TL;DR first, return to full chapter if needed

Every chapter begins with an expandable Chapter Summary (TL;DR) containing context, key frameworks, what works, what fails, and the practical takeaway. Many practitioners use TL;DRs for 80% of their needs and read full chapters only when implementing specific tools.

Choose Your Path

Select the pathway that matches your role and immediate needs:

State/Local Health Departments

“I need to evaluate AI tools my agency is considering”

Start here: - AI Fundamentals - What AI actually is - Evaluation Framework - Audit vendor claims - Ethics & Privacy - Critical considerations

Epidemiologists / Public Health Workforce

“I want to use AI in my surveillance or public health research work”

Start here: - Disease Surveillance - AI for outbreak detection - Forecasting - Predictive models - Practical Tools - Hands-on code - Using LLMs: Theory & Practice - ChatGPT, Claude, Copilot for analysis

Jump to: Case Studies for real implementations

Policymakers / Public Health Leadership

“I need to make informed decisions about AI adoption”

Start here: - Global Health Perspectives - Strategic overview - Policy & Governance - Frameworks and regulations - Ethics & Fairness - Responsible AI principles - AI Misinformation - Combat health misinfo

Health Communication / Behavioral Health

“I work in health communication, behavior change, or community outreach”

Start here: - AI Misinformation - Detect and counter health misinfo - Behavioral Interventions - AI chatbots, personalized messaging - Using LLMs: Theory & Practice - Communication assistance tools

Focus: Messaging, behavior change, combating misinformation, personalization

Frontline Healthcare Workers/ Clinicians

“I’ll be using AI tools in direct patient care or community health”

Start here: - Clinical AI Applications - Real-world use cases - Deployment & Workflow - Integration strategies - Evaluation - Assessing tool reliability

Focus: Practical usability, workflow integration, patient-facing implications

AI Developers/ Engineers/ Researchers (Technical)

“I’m building or customizing AI systems for public health”

Start here: - Data Quality & Pipelines - Public health data requirements - Practical Toolkit - Code examples and tools - AI-Assisted Coding - GitHub Copilot, Cursor, development tools - Model Evaluation - Validation frameworks

Focus: Technical limitations, data requirements, model behavior

New to AI entirely? Read Part I: Foundations sequentially.

Acknowledgments

This handbook wouldn’t exist without the work of public health professionals who’ve been doing this work for decades.

Thanks to colleagues at the Nebraska Department of Health and Human Services, the Centers for Disease Control and Prevention, the World Health Organization, the Africa CDC, and the Nebraska Public Health Laboratory. Thanks to state and local health departments whose work shapes what effective public health practice looks like.

Thanks to the CDC’s Advanced Molecular Detection (AMD) program, the SPHERES consortium, StaPH-B (State Public Health Bioinformatics), and the Association of Public Health Laboratories (APHL) for building the pathogen genomics and bioinformatics infrastructure that powers modern surveillance.

Thanks to Nextstrain for the phylogenetic tools that enable real-time genomic epidemiology worldwide.

Thanks to the National Academy of Medicine and the University of Nebraska Medical Center’s Global Center for Health Security for frameworks that shape how we approach health security and emergency preparedness.

And thanks to the open-source community, including the Epidemiologist R Handbook team, for tools that make public health practice more accessible.

Bryan Tegomoh, MD, MPH Berkeley, California October 2025

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