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DEMICS

An annotated reading list on the convergence of epidemiology, network theory, and the virality of information.

Published onMay 28, 2020
DEMICS

In 1949, the computer pioneer John von Neumann published Theory and Organization of Complicated Automata in which he identified the virality of a computer program as its ability to self-replicate.1 Two decades later, Frederick Cohen proved this theory with a mathematic equation, subsequently coining the term ‘computer virus’ as ‘a program that can infect other programs by modifying them to include a, possibly evolved, version of itself.’2 Today, computer viruses have evolved into advanced persistent threats (APTs) expertly designed to elude detection, as has the tendency to describe cybersecurity phenomena using metaphors from infectious disease.

Language such as “antivirus pattern matching,” “code sanitization,” “index cases,” “infected nodes,” and “vulnerable networks” is now endemic. The epidemiological metaphors of everyday software parlance are thus well established. What is less recognized, however, is the flip side of this analogy: that innovations within network theory, cybersecurity, and data science are revolutionizing the study of infectious disease. In the throes of a global pandemic, the moment is ripe to deploy methods and theories from network theory to combat disease spread. One cannot evade the irony of this timely inversion wherein such innovations might provide solutions to issues that are, at times, their namesake.

What follows is an initial attempt to demonstrate the reflexivity of paradigms in network theory and epidemiology. This exercise is anchored in the titular suffix -demics, which stems from the Greek word for populations demos. This shared etymon makes for a fruitful comparison as it signals the intrinsically social import of both fields: from the transmission of disease in pandemics to the dissemination of knowledge in academia, and more recently, the ostensibly viral edema of disinformation online, one’s approach to each of these subjects is determined by the nature of spreading phenomena.

By presenting possible avenues for their convergence, this reading list seeks to spawn new perspectives for tackling pressing issues across a range of adverse environments from disease spread, cybersecurity in public health data, the virality of online disinformation, and more. In other words, only by leveling these disciplines do we discover actionable revelations that can be funneled into an integrative panacea for these post-normal times. This reading list consists of four cross-analyses that can be categorized under the following headings: 1) Epidemiology for Network Theory; 2) Public Health + Big Data; 3) Infodemics: the Structural Virality of Disinformation; 4) Viral Knowledge in the Post-Digital Age.

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