An introduction to the methodological foundations of epidemiology — and to the seven steps that make studies matter for the health of populations.
Fig. 1. The Isle of Farrlandia — the book’s teaching population, named for William Farr, founder of modern vital statistics. Every method in the book is tested against a population whose truth you can see.
“The death rate is a fact; anything beyond this is an inference.”William Farr, 1807–1883
Fig. 2. Weekly reported cases, Port Snow outbreak. Chapter 4 traces this curve from the first count to the last inference.
Learn epidemiology by doing. Manipulate populations, watch associations form, introduce bias on purpose, and see exactly what it does to your estimates. No formulas required to start — just curiosity.
Replace a lecture with a live demonstration. Preset scenarios show confounding, Simpson’s paradox, and selection bias unfolding in real time, and each interactive exercise is keyed to a chapter of the book.
Epidemiology shapes policy, headlines, and daily life. Understand how we come to know what causes disease — and why it matters who we study, how we study them, and who gets counted.
The book organizes epidemiologic thinking around seven foundational steps. Each builds on the last; together they form a complete route from a question worth asking to an answer worth acting on.
Seven decisions, made deliberately, are what separate a study that is merely published from a study that matters.
Each tool maps directly to the book. Use them in class, assign them as exercises, or explore on your own. The print edition includes QR codes linking to each module.
Build a data-generating process. Add confounding, measurement error, and selection bias, and watch them distort causal estimates. Five preset scenarios for classroom use.
Adjust disease and exposure in a population of Farrlandians. Watch prevalence, risk ratios, and risk differences update in real time.
Toggle causes for individual Farrlandians. Disease occurs only when a sufficient cause is complete: different people, different pathways, same disease.
Walk through all seven steps to design your own epidemiologic study of consequence, from defining a population to judging how far your answer travels.
One thousand Farrlandians with full profiles. Hover to meet them. Filter, stratify, and compute associations from the living dataset behind every example.
Compare causal effects across populations with different distributions of component causes, and see why the same exposure can matter more in one place than another.
Adjust sensitivity, specificity, and prevalence, and watch predictive values change. Encounter lead-time and length bias through animation.
Methods are never neutral. In every chapter, a Consequence Box connects one methodological choice to a question of social responsibility — because how we study health decides whose health we see.
How we define a health indicator determines who counts as a case. Who counts determines what risk factors we find. What we find determines what gets funded.
Consider depression. A clinical interview identifies fewer cases than a screening tool — and those missed are disproportionately people without access to clinical settings. The measurement choice is not neutral.
When you choose a measure, ask: whose health does this make visible, and whose does it obscure?
An inter-school competition for public health students. An outbreak has struck Farrlandia: investigate it, identify its causes, propose interventions — and compete against teams from schools across the country.
Get word when new tools launch, when the Farrlandia Challenge opens for registration, and when the second edition publishes.