Welcome
The Public Health Analytics Playbook is a collection of data-driven analyses exploring real public health issues. Each chapter takes on a specific topic, pulls data from public sources, walks through the epidemiology, and provides reproducible R and Python code you can adapt for your own work.
This is equal parts curiosity project and practical reference. The analyses are grounded in published data and official statistics. The code is designed to be copied, modified, and reused.
This playbook is a work in progress. New chapters are added as research is completed. Each chapter is self-contained: you can read them in any order, skip to whatever issue interests you, and pull code directly into your own projects.
0.1 What This Is
Each chapter in the playbook follows a consistent pattern:
- The issue: What is the public health problem, and why does it matter?
- The data: Where does the evidence come from, and how do we access it programmatically?
- The analysis: Reproducible code (R and/or Python) that explores the data, with narrative interpretation
- The visualizations: Charts, maps, and tables that make patterns visible
Some chapters may compare the same issue across jurisdictions (U.S. states, countries, tribal nations). Others may go deep on a single geography. The scope is flexible and issue-driven.
0.2 Who This Is For
- Epidemiologists and public health analysts looking for reproducible examples they can adapt
- Data scientists curious about public health data sources, APIs, and visualization
- Public health students seeking applied analytics beyond textbook exercises
- Anyone who wants to understand what the data actually shows about the health issues that shape our communities
0.3 Companion Projects
This playbook is part of a suite of Intersect Collaborations resources:
- Public Health Automation Clinic: Practical automation solutions for public health workflows
- Bridgeframe Toolkit: Bridging business analysis and public health terminology and practice