disease interacts with the progress of another. For example, HIV and tuberculosis
have been identified as coinfective: many more people have both diseases than
would be predicted by two independent epidemic models. Elisa F. Long and her
coauthors (2008) have proposed a variation of the basic compartmental model
along Kermack-McKendrick lines that allows for people infected by one of these
diseases to be more likely to catch and spread the other.7 Models like this one
could represent narrative constellations in which multiple narratives support one
another by contagion. Such models could also represent the interaction of
economic narratives, such as the technological unemployment narrative, with
economic status, such as unemployment.
Structural macroeconomic models commonly include simple univariate
autoregressive integrated moving average (ARIMA) models to represent error
terms or driving variables for which there is no economic theory. George E. P.
Box and Gwilym Jenkins first popularized the ARIMA models in a 1970 book.
While Box and Jenkins described these models as useful in any realm of science,
economists have used them most aggressively.8 Owing to a well-developed
theory of forecasting of times series that can be described in ARIMA terms, the
epidemic among economists of ARIMA models led to a slightly delayed
epidemic of rational expectations models, which peaked (according to Google
Ngrams) around 1990 but still remains prevalent today. The ARIMA models are
an alternative to the compartmental models described in this appendix. But there
is something essentially arbitrary about the ARIMA models, which, unlike the
compartmental epidemic models, lack a theoretical underpinning.9
The ARIMA methods can be improved with the theoretical epidemic models,
using a combination of simulation, classification, statistical and optimization
techniques to forecast the epidemic curve when contagion rates and recovery
rates vary through time.10 We can selectively bring in data other than data on the
epidemic itself based on our knowledge of the structure of epidemics, and this
takes us well beyond the mindless search for “leading indicators.”
Not all data on epidemics fit the compartmental model framework well.
Consider the long-slow US epidemic of poliomyelitis enterovirus cases from the
late nineteenth century to their peak in 1952, superimposed on seemingly
random one-summer epidemics. A gradual trend toward better cleanliness and
hygiene should have had the effect of reducing the incidence of the disease, not
increasing it. Paradoxically, the lower incidence of the disease, which was in
most cases benign, had the effect of making reported cases involving paralysis

or other consequences more common because nursing infants were less likely to
receive antibodies from their mothers, which would have helped them gain
immunity to the disease’s severe consequences in later reinfections.11
When we apply the compartmental model to social epidemics and to
epidemics of ideas, certain changes seem natural. One thought is that the
contagion rate should decline with time, as the idea becomes gradually less
exciting. One way of modeling that notion comes from Daryl J. Daley and David
G. Kendall (1964, 1965), who said that the Kermack-McKendrick model could
be altered to represent the idea that infectives might tend to become uninfective
after they meet another infective person or a recovered person, because they then
think that many people now know the story. Because the story is no longer new
and exciting, the newly uninfected choose not to spread the epidemic further.
D. J. Bartholomew (1982) argued that when we apply variations of the
Kermack-McKendrick model to the spread of ideas, we should not assume that
ceasing to infect others and forgetting are the same thing. Human behavior might
be influenced by an old idea not talked about much but still remembered, or
“behavioral residue” (Berger, 2013).
There is now a substantial economics literature on network models, including
the recent The Oxford Handbook of the Economics of Networks (Bramoullé et
al., 2016). There are only a few behavioral epidemic models. The word narrative
does not appear even once in the Handbook. Some of these modified SIR models
involve complex patterns of outcomes and sometimes cycles. Geographic
models of spread are increasingly complicated by worldwide social media
connections.12
Some SIR models dispense with the idea of random mixing and choose
instead a network structure.13 There may be strategic decisions whether to allow
oneself to be infected, and the fraction of the population infected may enter into
the decision (Jackson and Yariv, 2005). Other models describe individuals as
adopting a practice not merely through random infection but rather through
rational calculations of the information transmitted through their encounters with
others.14
The core Kermack-McKendrick model may apply no matter how people
connect with one another despite concerns that modern media (especially the
Internet) make the original SIR model less accurate in describing social
epidemics. For this reason, using the SIR model to explain the spread of ideas or
narratives may require modifying it to take account of contagion by broadcast as