The Rules of Contagion

To be clear, there are some very good researchers within the wider
CDC, and the Ebola model was just one output from a large research
community there. But it does illustrate the challenges of producing
and communicating high profile outbreak analysis. One problem with
flawed predictions is that they reinforce the idea that models aren’t
particularly useful. If models produce incorrect forecasts, the
argument goes, why should people pay attention to them?
We face a paradox when it comes to forecasting outbreaks.
Although pessimistic weather forecasts won’t affect the size of a
storm, outbreak predictions can influence the final number of cases. If
a model suggests the outbreak is a genuine threat, it may trigger a
major response from health agencies. And if this brings the outbreak
under control, it means the original forecast will be wrong. It’s
therefore easy to confuse a useless forecast (i.e. one that would
never have happened) with a useful one, which would have
happened had agencies not intervened. Similar situations can occur
in other fields. In the run up to the year 2000, governments and
companies spent hundreds of billions of dollars globally to counter the
‘Millennium bug’. Originally a feature to save storage in early
computers by abbreviating dates, the bug had propagated through
modern systems. Because of the efforts to fix the problem, the
damage was limited in reality, which led many media outlets to
complain that the risk had been overhyped.[65]
Strictly speaking, the CDC Ebola estimate avoided this problem
because it wasn’t actually a forecast; it was one of several scenarios.
Whereas a forecast describes what we think will happen in the future,
a scenario shows what could happen under a specific set of
assumptions. The estimate of 1.4 million cases assumed the
epidemic would continue to grow at the exact same rate. If disease
control measures were included in the model, it predicted far fewer
cases. But once numbers are picked up, they can stick in the
memory, fueling scepticism about the kinds of models that created
them. ‘Remember the 1 million Ebola cases predicted by CDC in fall
2014,’ tweeted Joanne Liu, International President of Médecins Sans
Frontières (MSF), in response to a 2018 article about forecasting.[66]
‘Modeling has also limits.’