JUDY WOODRUFF: Even as
more states are trying to
reopen their economy, a new
"PBS NewsHour"/NPR/Marist

poll found that 77 percent of
Americans worry about a second
wave of infections yet to come.

This comes as computer-based
models suggest that the U.S.
will pass its own grim milestone

by June, 100,000-plus deaths.
That higher projection is
arriving even sooner than some

of the models estimated
just weeks ago.

But models are not crystal
balls. The work that goes into
making them and their ultimate

purpose is more complicated
than you might be able to
tell from the headlines.

Miles O'Brien explains in
his latest report for our
series the Leading Edge.

MILES O'BRIEN: We live in
a complicated world, filled
with more data than insight.

Finding a path to clarity is
not easy, even on a good day.
And these are not good days.

So, how can we take a huge
amount of data and make it
understandable, so we can see

the future?

BETZ HALLORAN, Fred Hutchinson
Cancer Research Center: You
can't believe every number that

comes out. But if we don't
try to formulate our thinking
about a complex process, then

we will be running blind.

MILES O'BRIEN: Betz Halloran is
an infectious disease modeler.
She writes mathematical formulas

that define the chaotic,
exponential spread of infection.

A biostatistician at Seattle's
Fred Hutchinson Cancer Research
Center, she's part of the

team that curates the
Global Epidemic and
Mobility Model, or GLEAM.

BETZ HALLORAN: The GLEAM model
is a big mobility model that
can answer global questions.

MILES O'BRIEN: GLEAM begins
with the first infection in
China and travels down the many

paths of exponential growth,
constantly calculating
who is susceptible,
exposed, infectious, and

recovered, S-E-I-R, or SEIR.

BETZ HALLORAN: You can structure
it in many different ways.
But, usually, when we talk

about infectious disease
modeling, that's the basic sort
of meat and potatoes of what's

going to be in a model.

MILES O'BRIEN: But the
model does not stop there.
It factors in the entire
global transportation

network, including airline
schedules and capacity.

BETZ HALLORAN: So, the
question we were asking
way back then was, where
is it going to spread?

If it gets into the United
States, where would it go first?

And once it gets in, then we
could use GLEAM to look at the
question of, how much is it

going to spread in the different
places? Where is it going to
go first? And then we predicted

that pretty well.

MILES O'BRIEN: Halloran
and her team did accurately
predict where COVID-19
would first surge in

the United States.

But, as the pandemic wore on,
the limitations of the models
became more evident. After all,

no one really knows how
the virus is transmitted,
who's likely to get sick
and who won't, who's

likely to die, who
might have immunity.

All those questions won't
be answered until there
is widespread testing.
So, in the meantime,

the models muddle on,
with sometimes dizzyingly
confusing results.

One of them, from Britain's
Imperial College, predicted two
million COVID-19 deaths in the

United States. But that
assumed no human response,
no social distancing.

BETZ HALLORAN: All models
are wrong, but some
models are helpful, and
I think it's important

to remember that.

MILES O'BRIEN: Nearby, at the
University of Washington's
Institute for Health Metrics

and Evaluation, they built a
much simpler model that started
with a specific question

in mind: Did the health
care system have the
capacity to treat a surge
of COVID-19 patients?

Chris Murray is the director.
He and his team wrote a model
that, unlike many others at

the time, factored in the
human response to the pandemic.

DR. CHRISTOPHER MURRAY,
Director of Health Metrics,
University Of Washington: If you

ignore the behavioral
response, you're going
to massively overshoot.

And so I think it is a
reasonable strategy to
try to look at models,
like the economists

do, which build in how
individuals, local government,
state government, are going to

respond to the problems
as they unfold.

DR. DEBORAH BIRX, White
House Coronavirus Response
Coordinator: So I'm
sure you're interested

in seeing all the states.

MILES O'BRIEN: Producing speedy
state-by-state results, with
consistently lower projections,

the University of Washington
model was frequently
cited by the White House
in daily coronavirus

briefings.

DR. DEBORAH BIRX: And I
think, if you ask Chris
Murray, he would say...

MILES O'BRIEN: But the model
initially assumed there would
be widespread adoption of social

distancing restrictions
in the U.S.

Once it became clear that
wasn't happening, the modeling
team went back to the drawing

board, releasing a new version
on May 4. It now uses mobility
data gleaned from cell phone

usage to better understand
how well people are complying
with the expert advice.

As a result, that model's
projection for the
total U.S. death toll by
August 4 from COVID-19

instantly went from
about 72,000 to 134,000.

DR. CHRISTOPHER MURRAY: It's
sensible to try to look at a
wide array of models and try

to look at how -- do they
tell you the same story?
Are they converging?

It's very confusing, I think,
for many decision-makers
to navigate through
some of the models.

MAN: We're going to start
off with this weekend.

MILES O'BRIEN: Weather
forecasters are some
of the most adept at
navigating the inherent

uncertainties of modeling.

MAN: Going to have some
travel problems if...

MILES O'BRIEN: After all, it's
been 70 years since they first
ran a model through a computer

to create a forecast. It's been
steady improvement ever since.
It's now possible to reliably

forecast seven days in advance
with 80 percent accuracy.

But, with a novel virus, there
are so many unknowns. And
weather models do not have to

account for human behavior.

Marshall Shepherd is director of
the Atmospheric Sciences Program
at the University of Georgia.

MARSHALL SHEPHERD, Atmospheric
Sciences Program Director,
University of Georgia: It's very

important, when consuming
these coronavirus models and
weather models, to consume the

uncertainty that we
know is inherent.

But we have a way to get
around that in weather
called ensemble modeling.

MILES O'BRIEN: Ensemble
modeling, meaning combining the
predictions of many different

models, it's a crucial tool that
has greatly improved forecasting
the weather and, in the

past three years, seasonal
influenza as well.

Nick Reich is an associate
professor of biostatistics
at the University of
Massachusetts-Amherst.

Working with the Centers for
Disease Control and Prevention,
he leads a team that builds

ensemble models to
improve predictions of
the spread of the flu.

NICK REICH, University
of Massachusetts-Amherst:
I don't think any one
model should be viewed

as gospel truth.

When you just use one model,
you end up with a too strong
reliance on one particular set

of assumptions and one
particular viewpoint. And
this is why it's really
critical to consider

multiple models together.

MILES O'BRIEN: The influenza
models are informed
by up to 20 years of
experience with the viruses

and the accuracy of the models.

Reich and his team have now
built a COVID-19 ensemble model.
But it, of course, does not

have the benefits
of a long backstory.

NICK REICH: We do have hundreds
of years of theory about how
to build mathematical models

of infectious disease, but
have they ever been tested in
real time in this way, with

all of the data sources that
are available to us? No.

We're building this car as it's
careening down the highway,
and we're learning about

these models as we go.

MILES O'BRIEN: Infectious
disease modelers are scrambling
to figure out where we are

headed, depending on
the decisions we make.

If we take the time to better
understand what the models can
and cannot do, maybe we will

do the same as we search for
the path back to normalcy.

For the "PBS NewsHour,"
I'm Miles O'Brien.