The Rules of Contagion

Models and Rash Decisions’. According to Cooper, several models
included questionable assumptions, with one particularly prominent
example. ‘Collective eyebrows were raised when the Centers for
Disease Control’s model completely neglected contact tracing and
forecast 77 trillion cases if the epidemic went unchecked,’ he noted.
Yes, you read that correctly. Despite there being fewer than 7 billion
people in the world at the time, the model had assumed that there
were an infinite number of susceptible people that could become
infected, which meant transmission would continue indefinitely.
Although the CDC researchers acknowledged it was a major
simplification, it was bizarre to see an outbreak study make an
assumption that was so dramatically detached from reality.[60]
Still, one of the advantages of a simple model is that it’s usually
easy to spot when – and why – it’s wrong. It’s also easier to debate
the usefulness of that model. Even if someone has limited experience
with mathematics, they can see how the assumptions influence the
results. You don’t need to know any calculus to notice that if
researchers assume a high level of smallpox transmission and an
unlimited number of susceptible people, it can lead to an
unrealistically large epidemic.
As models become more complicated, with lots of different
features and assumptions, it gets harder to identify their flaws. This
creates a problem, because even the most sophisticated
mathematical models are a simplification of a messy, complex reality.
It’s analogous to building a child’s model train set. No matter how
many features are added – miniature signals, numbers on the
carriages, timetables full of delays – it is still just a model. We can use
it to understand aspects of the real thing, but there will always be
some ways in which the model will differ from the true situation.
What’s more, additional features may not make a model better at
representing what we need it to. When it comes to building models,
there is always a risk of confusing detail with accuracy. Suppose that
in our train set all the trains are driven by intricately carved and
painted zoo animals. It might be a very detailed model, but it’s not a
realistic one.[61]