Viral Networks: Connecting Digital Humanities and Medical History ed. by E. Thomas Ewing and Katherine Randall (review)

If you do not know anything about edges and nodes, Cytoscape, or epistemic network analysis, this book is for you. It is a great resource for understanding the basics of quantitative methods and tools applied to the history of science and medicine, by showcasing nine ongoing research projects ranging from the Middle Ages to the twentieth century.

The book has a hybrid shape, available both in print and as an open access digital EPub or PDF. The editor makes available the accompanying raw data files or high-definition visualizations (however, the links to the downloadable resources seem only to be visible via the EPub version, not the PDF). Because the book stemmed from a series of workshops, it is now part of an ongoing interdisciplinary conversation. After its publication in 2018, a "Reconnected" workshop took place in 2019. The workshop's video recordings are archived online and act as a "sequel" to the book, reactivating the main topics and offering follow-ups to some of the projects. It is a formidable way of proceeding beyond the published material and keeping the project alive on the Web.

The volume includes a very lucid and meaningful introduction (Ewing and Randall) that gives context to the project. It is followed by ten chapters, nine of which cover different ways to tackle network analysis using data from the history of medicine. I would advise readers not accustomed to using quantitative methods for research to jump to chapter 10 (Nathaniel D. Porter) as it aptly outlines the essential strategies for getting started with network analysis and works as a concise how-to guide. It is equally useful to consult the glossary of network terminology. While I did come across a rather elaborate notion—the Latent Dirichlet Allocation—which was missing from the glossary, going through these definitions will enable the reader to navigate the research matter more comfortably.

Sarah Runcie (chap. 1) addresses mobile health issues in colonial Cameroon by applying network analysis to a set of medical records and by questioning the visible and the invisible within the data: references to Cameroonian medical personnel were often absent from records, even though they were instrumental in the field work. Kylie Smith (chap. 2) investigates networks of segregation in mid-twentieth-century Alabama psychiatric hospitals. Katherine Sorrels (chap. 3) takes us back to a network of Viennese physicians and intellectuals (Konig, Asperger, Kanner and Bettelheim), inquiring whether the study of citation networks can shed some light onto the philosophical, spiritual, and humanistic roots of the early medical research of the intellectual and developmental disabilities (IDDs). Lukas Engelmann (chap. 4) studies nineteenth-century plague outbreak reports when statistics were not yet a central tool for epidemiologists and physicians still focused on producing complex narratives. Here useful results can be obtained from basic word counting, while more refined models are to be implemented. Michelle DiMeo and A. R. Ruis (chap. 5) pertinently address the necessity for humanities scholars to "collaborate with machines" and provide useful leads on how to prepare digitized data (in this case, the copious correspondence of Early Modern polymath Samuel Hartlib) for future epistemic network analysis. Katherine Cottle (chap. 6) employs network analysis and medical vocabulary metaphorically to "anatomize" a body of epistolary exchanges between two medical practitioners. Nicole Archambeau (chap. 7) guides the reader back into the Middle Ages, for a study of plague references in fourteenth-century Latin documents, demonstrating that network analysis can challenge or reframe preliminary research questions. Ruis (chap. 8) creates an elegant model allowing us to follow the evolution of "nutrition": the epistemic network analysis of its early definitions reveals how the concept shifts across time. Christopher J. Phillips (chap. 9) uses publication networks to research how a group of statisticians from the National Institutes of Health helped popularize statistics in medical research, thus pushing toward the advent of...