major causes of these
global problems are ignored, hampering our ability to
take action against them. We cannot get into a situation where no one listens
anymore. Without trust, we are lost.
And hotheaded claims often entrap the very activists who are using them.
The activists defend them as a smart strategy to get people engaged, and then
forget that they are exaggerating and become stressed and unable to focus on
realistic solutions. People who are serious about climate change must keep
two thoughts in their heads at once: they must continue to care about the
problem but not become victims of their own frustrated, alarmist messages.
They must look at the worst-case scenarios but also remember the uncertainty
in the data. In heating up others, they must keep their own brains cool so that
they can make good decisions and take sensible actions, and not put their
credibility at risk.
Ebola
I described in chapter 3 how, in 2014, I was
too slow to understand the
dangers of the Ebola outbreak in West Africa. It was only when I saw that the
trend line was doubling that I understood. Even in this most urgent and fearful
of situations though, I was determined to try to learn from my past mistakes,
and act on the data, not on instinct and fear.
The numbers behind the official World Health Organization and the US
Centers for Disease Control and Prevention (CDC) “suspected cases” curve
were far from certain. Suspected cases means cases that are not confirmed.
There were all kinds of issues: for example, people who at some point had
been suspected of having Ebola but who, it turned out, had died from some
other cause were still counted as suspected cases. As fear of Ebola increased,
so did suspicion, and more and more people were “suspected.” As the normal
health services staggered under the weight
of dealing with Ebola and
resources had to move away from treating other life-threatening conditions,
more and more people were dying from non-Ebola causes. Many of these
deaths were also treated as “suspect.” So the rising curve of suspected cases
got more and more exaggerated and told us less and less about the trend in
actual, confirmed cases.
If you can’t track progress, you don’t know whether your actions are
working. So when I arrived at the Ministry of Health in Liberia, I asked how
we could get a picture of the number of confirmed cases. I learned within a
day that blood samples were being
sent to four different labs, and their
records, in long and messy Excel spreadsheets, were not being combined. We
had hundreds of health-care workers from across the world flying in to take
action, and software developers constantly coming up with new, pointless
Ebola apps (apps were their hammers and they were desperate for Ebola to be
a nail). But no one was tracking whether the action was working or not.
With permission, I sent the four Excel spreadsheets home to Ola in
Stockholm, who spent 24 hours cleaning
and combining them by hand, and
then carrying out the same procedure one more time to make sure the strange
thing he saw wasn’t a mistake. It wasn’t. When a problem seems urgent the
first thing to do is not to cry wolf, but to organize the data. To everybody’s
surprise, the data came back showing that the number of confirmed cases had
reached a peak two weeks earlier and was now dropping. The number of
suspected cases kept increasing. Meanwhile, in reality, the Liberian people
had successfully
changed their behavior, eliminating all unnecessary body
contact. There was no shaking hands and no hugging. This, and the pedantic
obedience to strict hygiene measures being imposed in stores, public
buildings, ambulances, clinics, burial sites, and everywhere else was already
having the desired effect. The strategy was working, but until the moment Ola
sent me the curve, nobody knew. We celebrated and then everybody
continued their work, encouraged to try even harder now that they knew what
they were doing was actually working.
I sent the falling curve to the World Health Organization and they
published it in their next report. But the CDC insisted on sticking to the rising
curve of “suspected cases.” They felt they had to maintain a sense of urgency
among those responsible for sending resources. I understand they were acting
from the best of intentions, but it meant that money and other resources were
directed at the wrong things. More seriously,
it threatened the long-term
credibility of epidemiological data. We shouldn’t blame them. A long jumper
is not allowed to measure her own jumps. A problem-solving organization
should not be allowed to decide what data to publish either. The people trying
to solve a problem on the ground, who will always want more funds, should
not also be the people measuring progress. That can lead to really misleading
numbers.
It was data—the data showing that suspected cases were doubling every
three weeks—that made me realize how big the Ebola crisis was. It was also
data—the data showing that confirmed cases were now falling—that showed
me that what was being done to fight it was working. Data was absolutely
key. And because it will be key in the future too, when there is another
outbreak somewhere, it is crucial to protect its credibility and the credibility
of those who produce it. Data must be used to tell the truth,
not to call to
action, no matter how noble the intentions.
