The Statistics of Blockchain for Public Health

Trust-minimised data integrity for health-sciences research

Welcome

This is the online version of The Statistics of Blockchain for Public Health by Ronald ‘Ryy’ G. Thomas, a graduate course textbook.

The organizing concept of the book is applications in public health. Every chapter opens with a public-health data problem, develops the blockchain mechanism and its mathematics as the means of addressing it, and returns to the problem to judge honestly whether the mechanism earns its cost. A single running case study, a multi-institution clinical-trial data-integrity ledger, threads the whole book, with vaccine cold-chain provenance as a recurring secondary example. The blockchain machinery is the means; the integrity of health data is the end.

Within that frame, we treat blockchain systems the way a statistician would want them treated: as objects of quantitative study rather than objects of belief. Consensus becomes applied probability, mining becomes a renewal process, an automated market maker becomes a convexity argument, and market narratives become an exercise in falsifiability. The mathematics the reader already speaks replaces the hand-waving that pervades most writing on this subject.

The book is designed as a ten-week, masters-level course. Each chapter maps to one teaching week and follows a fixed structure (learning objectives, orientation, the statistician’s contribution, worked content with check-your-understanding callouts, an LLM-collaboration section, exercises, and further reading). Every code example is written in R and executes at build time.

The intended reader is a first-year graduate student in public health, biostatistics, or epidemiology, with the probability and R background of a first-year masters student. No prior exposure to cryptography, distributed systems, or cryptocurrency is assumed. A public-health data problem opens every chapter, and the reader will find that the field’s load-bearing mathematics (waiting times, sampling designs, concentration measures, forecast calibration) is mathematics they have already met in their own discipline.

License

This book is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International.

Code samples are licensed under Creative Commons CC0 1.0 Universal, i.e. public domain.