Contagion, Recovery, and Decline
Efforts to lower contagion rates by avoiding contact with sick people are hardly
new. The history of quarantines extends back at least to 1377 when the city of
Venice imposed during a plague a thirty-day isolation period on arrivals by sea,
and then a forty-day isolation period for travelers by land (the word quarantine
derives from the Latin word for forty). The world has also seen occasional
attempts to increase contagion as an act of war, as with the catapulting of dead
bodies of plague victims into a fortified city at the Siege of Caffa, 1346.2
Another mechanism for a declining contagion rate is a decrease in the pool of
susceptible people. This pool decreases through time because many people who
had the disease are now immune to it (or dead). This mechanism, modeled in the
appendix (p. 289), occurs even if no health-care workers take action to contain
the disease, as in long-ago epidemics before modern medicine. Eventually, those
epidemics ended before everyone was infected.
When the contagion rate is lower than the recovery rate plus the death rate,
the disease does not disappear immediately. The contagion rate is not reduced to
zero. All that is necessary to conquer the epidemic is to lower the contagion rate
below the recovery rate. Unless the contagion rate is zero, there will still be new
cases of the disease, but the total number of sick people declines, gradually
tailing off to zero, at which point the epidemic ends.
We are talking here of the average contagion rate and average recovery rate,
averaging over many people. However, both the contagion rate and the recovery
rate can differ greatly from one individual carrier to another. A relatively small
percentage of super-spreaders can infect many people. One such super-spreader
was Mary Mallon, “Typhoid Mary,” who a century ago spread typhoid fever to at
least 122 people over an interval of years.3 In the context of narratives, most of
us may not be contagious enough for long enough to cause an epidemic without
the presence of these super-spreaders, and because of a small fraction of super-
spreaders the average contagion rate can be much higher than the typical
contagion rate. Today’s narrative super-spreaders may be enabled by marketing
using accelerated analytics, such as recently provided by NVIDIA Corporation
or Advanced Micro Devices, Inc., which is invisible to most of us. So we can’t
always accurately judge the contagiousness of a narrative by our own fascination
with it.

Both the appearance of the disease epidemic at a given time and place, and
the decline in the epidemic after its peak tend to be mysterious. Many factors
influence the contagion rate and recovery rate, factors that may be hard to
document. For example, the ultimate reason for the recovery could be a change
in the weather, which is more readily documented, or it could be a decrease in
the number of encounters between people that allow for transmission of the
disease, which might be hard to document. Or it might be some combination of
the two. The changes need not be big or obvious.
We can apply this same model to epidemics of economic narratives.
Contagion occurs from person to person through talk, whether in person or
through telephone or social media. There is also contagion from one news outlet
and talk show to another, as they watch and read one another’s stories. Once
again, the ultimate causes of the epidemic might not be obvious. Fortunately,
most economic narratives do not result in deaths, but the basic process is the
same. The “recovery plus deaths” variable in the medical model is simply
recovery, loss of interest in the narrative, or forgetting in the economic model we
are developing. Economic narratives follow the same pattern as the spread of
disease: a rising number of infected people who spread the narrative for a while,
followed by a period of forgetting and falling interest in talking about the
narrative.4
In both medical and narrative epidemics, we see the same basic principle at
work: the contagion rate must exceed the recovery rate for an epidemic to get
started. For example, when Ebola is found to have infected hundreds of people
in one town and virtually nobody in another, the explanation could be some
inconspicuous factor that made Ebola contagion rates higher in Town #1 than in
Town #2, putting the Town #1 contagion rate above the recovery rate at the
beginning of the epidemic. Meanwhile, in Town #2, there is no epidemic because
the contagion rate isn’t quite high enough to offset recovery. Similarly, with
narrative epidemics there may be two different narratives, one with some minor
story details that make it more contagious than the other. The minor story details
make the first narrative, and not the second, into an epidemic. Let’s apply this
insight to the Bitcoin narrative.