Not a bad introduction to epidemiology (at least from the perspective of a non-epidemiologist). Given the current news, at the very least, a "short introduction" to subject has now become a necessity for the average citizen to understand, to at help wade through the plethora of information that is being bombarded to them via media and other sources. This book hits the spot for that use-case, as well as a more specialized use-case focused for an introductory data scientist, who may profit from a short introduction the field, which will double as an introduction to experimentation and it's associated design, as well as very basic statistical reasoning.
This use-case works so well because in a true sense, epidemiology, and other subfields within the biological/medical sciences, like population genetics, and bioinformatics, actually inform the nature and development of data science in it's first decade of existence by virtue of the fact that many people in these subject-matters transitioned over to the profession seeking more lucrative (and exciting) work. One can see this most directly, in the nature of experimentation with respect to web-traffic, and how this problem-setup had traditionally been contextualized almost as a 1:1 port from epidemiological experimentation.
The book itself is a clean introduction to these notions, with material taken mostly from the methodological area vs. the medical/genetics domains. Use-cases or data presented are mostly generic tabular data that require no knowledge of medicine/biology etc., and the analysis/methods introduced could really be taken from any use-case in almost any domain that dealt with samples of populations.
That being said, all of the essentials are here, from a short intro to randomized control trials, dealing with populations, and how one may aggregate individual-level data to a higher-level unit, and what parameters one may extract from that process to run statistical analysis and/or experimentation, correlation, conditions of causation (from the classical standpoint), survival analysis, study of cohorts, experimentation, and meta analysis.
Most of these short-introduction books have been poor, yet I find this topic to be the perfect mix of intellectual rigor and common-day relevance to make this summary very good to both the general and purpose-driven reader. For the general reader, I think they will be well-equipped to look at contemporary news articles on the nature of Covid-19 (or any future pandemic) with a more informed critical eye, and the topics mentioned here easily extend to more formal text on the material and/or a introduction to statistics textbook. Recommended.