Post I

Re-publishing the post on Epidemiological books I like
Author

John Grady Heller

Published

August 31, 2023

Modified

November 10, 2024

Here are some books that I found very helpful or particularly informative for topics that I needed to study (often outside of classes and formal teaching environments) because of the high expectations concerning expertise in the field. I am not applying any specific criteria to my selection, other than I enjoyed them or learned a lot from them. If I want to go more in-depth for a book, that will probably be its own separate post.

This list will only contain books or textbooks - I feel like they don’t get as much love and are harder to promote than journal articles.

I hope you find it helpful.

The List

  • Monitoring the Health of Populations (2004) by Brookmeyer & Stroup. If you work with surveillance, this is a great resource and introduced methods that I haven’t often seen elsewhere. It focuses on temporal and spatial factors, epidemics, and use of public health surveillance data.

  • Understanding Advanced Statistical Methods (2013) Westfall & Henning. This book does a pretty good job of presenting important statistical concepts that may have been skipped over in stats classes. I find this more and more helpful as I advance in my career, because once you are out of the controlled environment of graduate school, there are so many different things you may need to use in your research. Even though I am not strictly a Biostatistician, I am often called on for statistical advice (often for things I haven’t seen before), and being more knowledgeable in general on how statics work at a fundamental level is virtually required.

  • Bayes Rules! (2021) Johnson, Ott & Dogucu. I struggled with understanding how to really apply Bayesian statistics until I found this book. It is very easy to understand for a statistics book, and yet does not waste your time and simply explains how to apply Bayesian methods. It provides a decent basic understanding of the math, and a great start for application. It’s completely free to access online at https://www.bayesrulesbook.com.

  • Handbook of Infectious Disease Analysis (2019) Held, Hens, O’Neill, & Wallinga. If you work with infectious diaereses, this book is full of the latest and greatest research. The authors are excellent, and if you really want the advanced methods with infectious diseases, this is a great book for references.

  • Handbook of Meta-Analysis (2021) Schmid, Stijnen, & White. This is similar to the handbook for infectious disease analysis, but it’s for meta-analyses. It is a great resource for up-to-date, advanced statistical methods in meta-analyses. Bayesian models, network meta-analyses, IPD, etc. You name it, the book introduces it and provides references.

  • Principles and Practice of Public Health Surveillance (2010) Lee, Teutsch, Thacker, & Louis. When I was working in surveillance, I found this to be very helpful. It filled in plenty of gaps that, for one reason or another, existed after all my coursework. It discusses a number of very relevant things for conducting surveillance, really how to “do” surveillance. It talks about meeting and teams structures, how focus on certain aspects of outbreaks, data management, and assessment of a surveillance system - many important things. If you’re feeling a bit lost on what to do in a public health surveillance role, this might help.

  • Modern Epidemiology - I probably don’t need to discuss this one; I think anyone who has any education in Epidemiology probably has already seen this book. If you haven’t, you probably should check it out but be ready for a pretty dense read.

  • A Handbook of Statistical Analysis Using R (2014) Hothorn & Everitt. This book is great if you are learning R for statistical applications. It focuses on base R, so it does not involve many packages you may encounter in your career, but if you are starting out (if you were taught with SAS or SPSS and R seems confusing) I think I prefer this to the O’Reilly texts that really focus on tidyverse and other kind of idiosyncratic applications. I would start here with R; base R always works, the environment of R packages is more like the wild west.

  • The Visual Display of Quantitative Information (1983) Tufte. I feel like this book is kind of a cult classic. I have to also admit I haven’t read Tufte’s later books (which have been highly recommended to me by experienced professors), but they seemed more artistic than practical - not a bad thing just not what I have been looking for. It talks about a number of concepts for drawing graphs and plots, with some respect for the artistry that can go into data representation. It’s honestly just fun if you like graphs, data, and a tiny bit of history.

Honorable Mentions

  • Statistical Rethinking (2015) McElreath. This book introduces Bayesian statistics for those who were more frequentist-trained. McElreath takes a long time to explain things through allegory, which just isn’t what I want in a math book. However, some will think this is more fun, and I can understand that. If you want to hear about the golem of Prague alongside your math, then go for it. If you just want to know how to do an analysis, see Bayes Rules! above. TLDR: McElreath is fun, but long-winded in my opinion. Many seem to really enjoy it though.

  • Theory of probability (1939) Jeffreys. I think of this as the OG Bayesian text. There have obviously been some changes in research since 1939 (like computers existing), but I have still found myself referencing the book for some statistical theory, even recently.

  • The Epidemiologist R Handbook (https://epirhandbook.com/). It might technically be or not be a book depending on semantics; it’s published to the preceding link as a markdown book. In any case, it is very helpful for public health epidemiology.

  • A Very Short Introduction To Epidemiology (2010) Saracci. I first learned about this book when helping to teach at the Johns’ Hopkins Center for Talented Youth; we used it as a main text for advanced highschoolers during a short summer course. It introduces the very basics of what Epidemiologists do, and might be interesting if you are or know someone who is interested in Epideiology, but who has no exposure to what it is.

  • Bayesian Data Analysis (2013) Gelman, Carlin, Stern, Dunson, Vehtari, & Rubin. This is a very well-known, respected, and thorough discussion on Bayesian methods and application. It is also the most dense book I have listed here on this page. That isn’t something to be afraid of, but understand that this is an advanced book that will absolutely require you to have a thorough understanding of math and statistics, and maybe a previous introduction to Bayesian analysis. I think of it as a logical next step after Bayes Rules!, or something to be approached in a course with a professor to help guide you. I should also point out that professor Vehtari’s course material, which focuses on the book’s material, can be found at his GitHub page (https://github.com/avehtari/BDA_course_Aalto) which may be helpful, too.