Usually when a novel disease, such as Coronavirus/COVID-19, is detected, people generally discuss it as if it’s spreading in a vacuum, independent of other diseases. But there are, in reality, numerous other contagions present in a given population infected with a given disease, and they frequently interact with each other. Now, in a new paper published in the journal, Nature, a team of researchers says that when you model multiple contagions spreading simultaneously rather than individually, an unorthodox, yet familiar, infection pattern emerges: the infection pattern of a meme.
The paper (via Futurism), is authored by Laurent Hébert-Dufresne, et al., and is based on research that was conducted at the Santa Fe Institute, beginning in 2015. The research was based on comparing a simulated model that described the way multiple, interacting contagions spread in human populations to a simulated model of the way social trends, such as a viral videos, spread.
Messaging/viral stories/misinformation have been shaping #COVID19. Contagions, social and biological, always interact. Why often ignore interactions in models and forecasts?@_jgyou @svscarpino and I looked at patterns of interacting contagions. (1/3)https://t.co/cKsOFYCBil— Laurent Hébert-Dufresne (@LHDnets) February 24, 2020
After comparing the two models, the researchers found that when they tracked the spread of the simulated interacting contagions, it looked “a lot like patterns of social reinforcement.” I.e. a lot like the way a meme, or other popular social trend, takes hold and goes viral.
According to the paper, both the pattern describing the spread of interacting contagions, as well as the pattern describing the spread of social trends, follow a “super-exponential pattern.” A super-exponential pattern, sometimes referred to as a J-curve or “hockey stick growth,” describes super-exponential growth, that, for at least some period of time, exceeds the rate of exponential growth. Standard exponential growth has a constant doubling time and thusly a constant bend on a graph; super-exponential growth displays (at some point or points) the “knee” of a curve, followed by a period where growth is almost completely vertical for a time.
An example of super-exponential growth. Wikimedia
In other words, the authors of the study have shown that simulated, interacting contagions actually work like a “complex contagion,” which is a phenomenon “in social networks in which multiple sources of exposure to an innovation are required before an individual adopts the change of behavior.”
Hébert-Dufresne described this complex contagion phenomenon to Science Daily, giving an example related to the way a popular movie can catch on. “[T]en friends telling you to go see the new Star Wars movie is different from one friend telling you the same thing ten times,” he’s quoted as saying in the article.
It's mostly mathematical descriptions and computer simulations but with a little bit of real data to illustrate that the results make sense.— Laurent Hébert-Dufresne (@LHDnets) February 27, 2020
In the biological world, this bombardment of movie recommendations from friends is likened to the simultaneous spread of interacting contagions: a given contagion, such as COVID-19, becomes far more infectious if it’s accompanied by other infectious diseases, such as the flu. This is because the flu can, for example, weaken people’s immune systems, thusly allowing COVID-19 to spread faster than it normally would. (Note that the authors are not focused on COVID-19 in their paper, and is only used here as our own example.)
Interacting contagions look a lot like patterns of social reinforcement.— Laurent Hébert-Dufresne (@LHDnets) February 24, 2020
This suggests we could use models from the social sciences to build models of interacting contagions whose complexity doesn't blow up with # of contagions. #complexity
An unexpected, especially interesting aspect of interacting biological contagions is the fact that they can interact with social contagions. One of the examples of this occurrence provided by the authors happened in 2017, when an ineffective Dengue vaccine—which would’ve been more effective had it been developed with interacting contagions taken into account—led to an anti-vaccination movement, which, in turn, led to a measles outbreak.
There does seem to be a big, possible upside to interacting biological contagions imitating the growth patterns of social trends, however. And that is the fact that social trends, such as a viral video or meme, eventually run their course, and fade from relevancy. Likewise, interacting biological contagions eventually run their course when they inevitably fail to find new hosts. Which means we should all hope COVID-19 goes the way of Nyan Cat.
What do you think about the way simulated interacting contagions imitate the way memes spread? Let us know your thoughts in the comments!