2018-12-01_Discover

The World Bank

U.S. government

European Union

Avian lu

(2003)

H1N1

(2009)

Ebola

(2014)

0 2 4 6 8 $10 billion

BILLIONS OF DOLLARS SPENT

Zika virus

(2015)

CHART: ALISON MACKEY/DISCOVER. VALERI POTAPOVA/SHUTTERSTOCK

December 2018^ DISCOVER^61

Pros

This model is based on objective facts

about animals, so predictions are

less prone to bias. And the model’s

predictions of risk are stable because

they’re based on biological traits

that aren’t likely to change anytime

soon.

Cons

The ability to predict any species’s

disease risk relies on how much we

know about it. So if we don’t have

enough information, the algorithm

has little to work with — and that

could lead to inaccurate predictions.

There’s also the problem of

follow-up. “It’s almost like selling

an insurance policy,” Han says.

Her model can produce a list of

potentially risky animals, but if no

one investigates them firsthand,

the prediction is just a prediction.

So in many cases, confirming the

model’s output takes some time.

UP NEXT

Han is working on figuring out how

to turn prediction systems like her

algorithms, which can be valuable

tools for researchers already focused

on sniffing out emerging diseases,

into something more proactive, such

as an early warning system. She’s

now focusing on what types of data

are necessary for such an alert system

and what still needs to be collected.

Working to give people a heads-up when diseases break out is useless

without resources to deal with the situation. Once experts predict a potential

outbreak, who funds the necessary preventive and containment measures?

And how much will they give?

Here’s a look at some of the major contributors and how much money

they’ve committed to fighting significant disease outbreaks.

NONPROFIT HELP

Though governments and

international institutions are

the main funders of outbreak

relief efforts, non-profits also

provide valuable money when

diseases strike. Here’s what

one of the biggest non-profit

contributors has given during

major outbreaks.

THE HIGH COSTS OF

FIGHTING DISEASE

OUTBREAK

The World

Bank

U.S.

government

European

Union

Zika virus (2015) $150 million $1.7 billion $52.17 million

Ebola (2014) $1.62 billion $5.4 billion $939.33 million

H1N1 (2009) $500 million $7.7 billion N/A

Avian flu (2003) $500 million $6.1 billion $241 million

OUTBREAK

The Bill and

Melinda Gates

Foundation

Zika virus (2015) $20.1 million

Ebola (2014) $53.1 million

H1N1 (2009) $5.6 million

Avian flu (2003) $18 million

examines a species that’s a question

mark — whether or not it carries disease

isn’t known beforehand — it can use what

it’s learned to study that species’s traits,

compare them with traits from known

carriers and predict the likelihood of that

species hosting a disease.

The algorithm can also create a list

of animals ranked by their risk of

carrying disease, as well as a description

of the traits that determine that risk.

For example, when Han trained the

algorithm with hundreds of mice species,

it determined disease-carrying risk was

associated with a rapid life cycle — early

sexual maturity, frequent reproduction

and fast growth rates. Knowing what

animals and which traits are most likely

to be associated with disease allows

researchers to zero in on and prepare for

where the next pandemic could originate.

THE BIG SPENDERS