David Spiegelhalter

In all the COVID-19 books that appear on bookshelves, Covid by Numbers has to be one of the most unique. It’s about the statistics behind the COVID-19 numbers – not just rates of infection, hospitalisation and mortality but how numbers and modelling were used to determine lockdowns and other rules. It also looks at the unexpected effects of the COVID-19 pandemic, such as the lowering of influenza and car accidents in young people.

If you’re not a statistician, don’t worry. Spiegelhalter and Masters explain the statistical methods very succinctly and clearly. (Honestly, I have never seen logarithmic scales in graphs explained in such a simple way that makes sense). The emphasis is on what the statistics mean and how they were used to make decisions regarding masks, lockdowns and who to vaccinate first. If you understand words, the graphs and tables are explained clearly. If you prefer pictures, the graphs and tables are there too. The content is heavily focused on the UK (Australia doesn’t get a mention) although there is some comparison with EU countries and with the US. To me, this didn’t matter but it might if you’re looking for a more global summary. But by keeping the statistics close to home, the authors are able to provide links to what was happening at the time on the ground as one of those affected.

Of course any COVID-19 book is going to be dated as soon as ink is put to paper. Covid by Numbers covers 2020 and up to May 2021, so it’s pre-Omicron and Delta and pre-oral treatments for COVID-19. Again, this didn’t really matter to me because the authors went into sub-topics of COVID-19 I’ve always wondered about. What is the effect on the economy (spoiler: they didn’t predict rapid inflation)? What are some of the effects of the lockdown? Why did deaths go down below ‘normal’ levels at times? I found this all fascinating, as these questions hadn’t really been covered for me before. What this book also shows is how far we’ve come in the nearly three years since COVID-19 was first reported. We have vaccines and treatments and have moved on from lockdowns. People have seen more statistics than they did in the preceding decade and the SIR model of infection is well known, as are the meaning of reproduction numbers.

This is a good read for looking at the COVID-19 pandemic from a different point of view. I’d love to see an updated version in years to come.

http://samstillreading.wordpress.com… (mehr)

An excellent book. I wish it had been written when I was a student of Economics. Instead of just giving statistical formulae, it explains the background to using them. It brings it to life with copious real world examples, many of which the author had been involved. In particular, the enquiries into Harold Shipman and child heart deaths at Bristol Royal infirmary. He exposes the fallacies behind popular newspaper scare stories, such as cancer risks. He makes the subject matter amusing as well as fascinating, and never in an intimidating way. It's also interesting to know the characters behind the different methods, such as Fisher (confidence levels); Neyman and Pearson (type 1 and 2 errors); and Bayes(inverse probability). The author admits favouring the last. This book should be on the reading list of every student, journalist and politician. At a time of Covid, never has it been more important to understand the bases of the many statistics debated.… (mehr)

After being disappointed by a couple of other statistics books for general audiences (Stigler's Seven Pillars of Statistical Wisdom and Abelson's Statistics as Principled Argument), I finally found what I was looking for. The first few chapters were a bit on the simple side but certainly entertaining enough to keep my interest up. For me the real rewards were in the later parts of the book where the author discusses slightly more advanced topics such as P-values, confidence intervals and Bayesian inference. I had briefly acquainted myself with these topics in my university studies but did not really understand their meaning back then. Without going into their mathematics, the author provides a nice overview of what these statistical concepts mean and what they don't mean. This somewhat philosophical review of practical statistical methods should be very useful for anyone who wants to be able to judge statistical conclusions critically. In the final chapters of the book the author also discusses how scientists sometimes misuse statistical methods, how such misuse can be detected and how newspapers and their readers should understand new scientific results when they are expressed in terms of statistics. All of which is very interesting, so I recommend this book.… (mehr)