# To find out more¶

The 1918 or “Spanish” flu is very much in the news close to its centenary. Spinney’s book is the definitive source [22].

The Black Death of the fourteenth century has had a huge number of histories written about it – and to show that history is a process and not a state, is still generating new works that encourage us to revisit both the sociology and the science. Ziegler addresses the full sweep [30]; Hatcher explores it from the perspective of a village [7]; while Sloane deals with a capital city [21]. The plague also had a unique and extensive effect on literature, being observed by many writers including the poet Petrarch, who wrote extensively of its effects on Florence [6]. An accessible yet detailed scientific treatment is still waiting to be written.

## To learn about epidemiology in practice¶

The European Centre for Disease Prevention and Control’s field epidemiology manual [1] is an open-source collaboration intended as a field guide and training resource for epidemiologists in the midst of an epidemic. A dose of reality on top of theoretical treatments.

As well as being one of the scientific pioneers, Albert-Lászl’o Barabási has written extensively and accessibly about complex networks and their applications. His book Linked: the new science of networks [3] is probably the best-known introductory work, with the follow-up on “bursty” processes [4] also well worth reading.

For a more social science perspective, Watts’ book on small worlds [27] explores issue such as rumour spreading and the ways in which different social structures can be understood mathematically.

## Textbooks and reference works on network science¶

The absolute best textbook on the mathematics of networks is that by Newman, another pioneer of the field [14]. Sayama deals with networks as part of a wider introduction to modelling complex systems [19]. Porter and Gleeson have produced a freely-available tutorial [17]. Kiss, Miller, and Simon’s book on epidemic spreading on networks is probably the most comprehensive recent mathematical treatment, and has some associated Python code [11].

## To do your own experiments¶

All the simulations done in this book use code that’s either contained in the book itself or available in public-domain libraries. All code, diagrams, and generated datasets for this book are available for download from the project’s GitHub repo, where you will also find the requirements.txt file needed to create a Python virtual environment capable of running everything (or indeed of re-creating the book in its entirety).

There’s nothing exclusive about science, so please feel free to download the code and run your own experiments – and then please share them, and your results, with the community! You’re then essentially engaging in the same processes of modelling, simulation, and experimentation as professional researchers.