Epidemiologists are using complex models to help policymakers
get ahead of the Covid-19 pandemic. But the leap from equations to decisions is
a long one.
People at a supermarket in Barcelona, Spain, practice social distancing
PHOTOGRAPH: DAVID RAMOS/GETTY IMAGES
IN THE PAST few
days, New York City’s hospitals have become unrecognizable. Thousands of
patients sick with the novel coronavirus have
swarmed into emergency rooms and intensive care units. From 3,000 miles away in
Seattle, as Lisa Brandenburg watched the scenes unfold—isolation wards cobbled
together in lobbies, nurses caring for Covid-19 patients in
makeshift trash bag gowns, refrigerated mobile morgues idling on the street
outside—she couldn’t stop herself from thinking: “That could be us.”
It could be, if the models are wrong.
Until this past week, Seattle had been the center of the Covid-19 pandemic in the United States.
It’s where US health officials confirmed the nation’s first case, back in
January, and its first death a month later. As president of the University of
Washington Medicine Hospitals and Clinics, Brandenburg oversees the region’s
largest health network, which treats more than half a million patients every
year. In early March, she and many public health authorities were shaken by an
urgent report produced by computational biologists at the Fred Hutchinson
Cancer Research Center. Their analysis of genetic data indicated
the virus had been silently circulating in the Seattle area for weeks and had
already infected at least 500 to 600 people. The city was a ticking time bomb.
The mayor of Seattle declared a civil emergency. Superintendents started
closing schools. King and Snohomish counties banned gatherings of more than 250
people. The Space Needle went dark. Seattleites wondered if they should be
doing more, and they petitioned the
governor to issue a statewide shelter-at-home order.
But Brandenburg was left with a much grimmer set of questions: How many people
are going to get hospitalized? How many of them will require critical care?
When will they start showing up? Will we have enough ventilators when
they do?
There’s
no way to know those answers for sure. But hospital administrators like
Brandenburg have to hazard an educated guess. That’s the only way they can try
to buy enough ventilators and hire enough ICU nurses and clear out enough
hospital beds to be ready for a wave of hacking, gasping, suffocating Covid-19
patients.
That’s
where Chris Murray and his computer simulations come in.
Murray
is the director of the Institute for Health Metrics and Evaluation at the
University of Washington. With about 500 statisticians, computer scientists,
and epidemiologists on staff, IHME is a data-crunching powerhouse. Every year
it releases the Global Burden of Disease study—an alarmingly comprehensive
report that quantifies the incidence and impact of every
conceivable illness and injury in each of the world’s 195 countries and
territories.
In
February, Murray and a few dozen IHME employees turned their attention
full-time to forecasting how Covid-19 will hit the US. Specifically, they were
trying to help hospitals—starting with the UW Medicine system—prepare for the
coming crisis. Brandenburg says the collaboration could turn out to be, quite
literally, life-saving. “It’s one thing to know you may be getting a surge of
patients,” she says. “If you can make that more tangible—here’s what it’s
actually going to look like—then we’re in a much better place in terms of being
able to plan for the worst.”
But
it’s a big if. During a pandemic, real data is hard to find. Chinese
researchers have only published some of their findings on the spread of
Covid-19 in Hubei. The ongoing catastrophe of testing for the virus
in the United States means no researcher has even a reliable denominator, an
overall number of infections that would be a reasonable starting point for
untangling how rapidly the disease spreads. Since the 2009 outbreak of H1N1 influenza,
researchers worldwide have increasingly relied on mathematical models, computer
simulations informed by what little data they can find, and some reasoned
inferences. Federal agencies like the Centers for Disease Control and
Prevention and the National Institutes of Health have modeling teams, as do
many universities.
As
with simulations of Earth’s changing climate or what
happens when a nuclear bomb detonates in a city, the goal here is to
make an informed prediction—within a range of uncertainty—about the future.
When data is sparse, which happens when a virus crosses over into humans for the first
time, models can vary widely in terms of assumptions, uncertainties, and
conclusions. But governors and task force leads still tout their models from
behind podiums, increasingly famous modeling labs release regular reports into
the content mills of the press and social media, and policymakers still use
models to make decisions. In the case of Covid-19,
responding to those models may yet be the difference between global death tolls
in the thousands or the millions. Models are imperfect, but they’re better than
flying blind—if you use them right.
THE BASIC MATH of a computational
model is the kind of thing that seems obvious after someone explains it.
Epidemiologists break up a population into “compartments,” a sorting-hat
approach to what kind of imaginary people they’re studying. A basic version is
an SIR model, with three
teams: susceptible to infection, infected,
and recovered or removed (which
is to say, either alive and immune, or dead). Some models also drop in an
E—SEIR—for people who are “exposed” but not yet infected. Then the modelers
make decisions about the rules of the game, based on what they think about how
the disease spreads. Those are variables like how many people one infected
person infects before being taken off the board by recovery or death, how long
it takes one infected person to infect another (also known as the interval
generation time), which demographic groups recover or die, and at what rate.
Assign a best-guess number to those and more, turn a few virtual cranks, and
let it run.
“At
the beginning, everybody is susceptible and you have a small number of infected
people. They infect the susceptible people, and you see an exponential rise in
the infected,” says Helen Jenkins, an infectious disease epidemiologist at the
Boston University School of Public Health. So far, so terrible.
The
assumption for how big any of those fractions of the population are, and how
fast they move from one compartment to another, start to matter immediately.
“If we discover that only 5 percent of a population have recovered and are
immune, that means we’ve still got 95 percent of the population susceptible.
And as we move forward, we have much bigger risk of flare-ups,” Jenkins says.
“If we discover that 50 percent of the population has been infected—that lots
of them were asymptomatic and we didn’t know about them—then we’re in a better
position.”
So
the next question is: How well do people transmit the disease? That’s called
the “reproductive number,” or R0, and it depends on how easily the
germ jumps from person to person—whether they’re showing symptoms or not. It
also matters how many people one of the infected comes into contact with, and
how long they are actually contagious. (That’s why social distancing helps; it cuts the
contact rate.) You might also want the “serial interval,” the amount of time it
takes for an infected person to infect someone else, or the average time before
a susceptible person becomes an infected one, or an infected person becomes a
recovered one (or dies). That’s “reporting delay.”
And
R0 really only matters at the beginning of an outbreak, when
the pathogen is new and most of the population is House Susceptible. As the
population fractions change, epidemiologists switch to another number: the
Effective Reproductive Number, or Rt, which is still the possible
number of people infected, but can flex and change over time.
You
can see how fiddling with the numbers could generate some very complicated math
very quickly. (A good modeler will also conduct sensitivity analyses, making
some numbers a lot bigger and a lot smaller to see how the final result
changes.)
Those
problems can tend to catastrophize, to present a worst-case scenario. Now,
that’s actually good, because apocalyptic prophecies can galvanize
people into action. Unfortunately, if that action works, it makes the model
look as if it was wrong from the start. The only way these mathematical oracles
can be truly valuable is to goose people into doing the work to ensure the
predictions don’t come true—at which point it’s awfully difficult to take any
credit.
Speaking at a White House briefing on
Thursday, Deborah Birx, response coordinator for the Coronavirus Task Force,
admonished the press against taking those models too seriously, even as New
York governor Andrew Cuomo begged for federal help with acquiring ventilators
and protective equipment for health care workers. “The predictions of the
models don’t match the reality on the ground,” Birx said.
Responding to Birx in a thread on
Twitter, Harvard infectious disease epidemiologist Marc Lipsitch said Birx had been
talking about work from his lab, which the federal government had asked for two
days prior. In a preprint (so not
peer-reviewed), his team had used an SEIR model with numbers tweaked to
simulate the tightening or loosening of social distancing measures, as well as
a potential flu-like seasonal variation in Covid-19 infections. He was varying R0, essentially. In the model, putting a stop to strict social
distancing (without something like a vaccine or a cure coming along) allowed
infections to climb right back up to their peak of about two critical cases per
1,000 people—which could be 660,000 Americans getting seriously ill or dying.
And even with the strictest lockdown-type measures lasting from April through
July, his team’s model finds that the disease surges back in autumn.
Remember, the whole point of social
distancing is to slow the epidemic, to keep the numbers of sick people at any
one time below the maximum that the health care system can handle and stall so
scientists can work on treatments. If Lipsitch’s team is right, the
characteristics of Covid-19 might require a cyclical flux between strict social
distancing and viral resurgence, on and on, perhaps until 2022. If everything
goes right, Lipsitch wrote—massive testing and quarantines of the ill, and aggressive
social distancing—it’ll be possible to keep numbers down and maybe shorten the
timeline. But, Lipsitch said on Twitter, he didn’t see any of that underway.
So in dismissing all that, was Birx making a policy
determination to assume that the model’s most Panglossian all-is-well
prediction will turn out to be correct? “That was my impression of her
comment,” said Yonatan Grad, an infectious disease epidemiologist at the
Harvard School of Public Health and a lead author with Lipsitch on that study,
speaking at a press conference on Friday.
Birx also took aim at an influential report published
earlier this month by disease modelers at Imperial College London that
predicted the deadly coronavirus could kill 500,000 Brits before the year was
out. These chilling forecasts jolted Boris Johnson’s UK government out of its plan to
sit back and wait for herd immunity to take the British Isles.
The report, which also predicted 2.2 million American deaths if the
government did nothing, got President Donald Trump’s attention. Shortly after,
the White House rolled out a 15-day social distancing
challenge, encouraging Americans to stay home as an act of
patriotism. Then the US economy tanked, and Trump got nervous about
staying the course. So when one of the Imperial researchers, Neil Ferguson,
brought new estimates to the UK parliament last Thursday that predicted a
British death toll below 20,000, Trump’s task force seized on the apparent
walk-back. “Half a million to 20,000,” Birx said during the Thursday press
briefing. “We are looking at that, in great detail, to understand that
adjustment.”
Except Ferguson wasn’t really walking
back his estimates or his model. As he explained later in his own series of tweets,
the new numbers resulted from two things: The UK government’s implementation of
social distancing measures, and a slightly higher R0 gleaned from new data from around Europe that
suggests the outbreak is moving faster than at first believed—so more people
are infected than anyone knows, with milder symptoms. Ferguson said this should
lend more evidence, not less, to the importance of social distancing measures.
To be clear, Ferguson was doing exactly what any good scientist would do
when presented with new data: updating the model. But these revisions came at a
politically dicey moment. Only days before, the British media had begun telling
people that maybe they didn’t have to worry after all. A new study said that
half the country had already contracted the coronavirus and were already immune
to it. This is, in fact, not what the study said.
But the two things hitting headlines at the same time created the public
perception that maybe the virus wasn’t so worrisome after all.
Produced by a group of researchers at Oxford, that other study had
examined the number of observed deaths that occured in Italy and the UK prior
to any social distancing interventions. The scientists then tried to ascertain
which hypothetical—emphasis on hypothetical—
circumstances could have led to those rapidly rising death tolls. One plausible
explanation, they found, is exactly what the Imperial group’s models suggest:
The virus has just begun to spread in the UK and it is causing severe symptoms
among a significant percentage of people. But equally plausible, according to
their models, is that SARS-CoV-2 could actually have been circulating since
January, possibly infecting up to half the population. For this scenario to
work, most people would only get a mild version of the disease—only a tiny
fraction of those infected would wind up in the hospital. In other words, in
the first scenario, the epidemic is just taking off. In the second, it’s
already swept through the population.
If it’s the second scenario, says Sunetra
Gupta, the theoretical epidemiologist who led the Oxford work, “that would be
great news,” because it would mean a substantial chunk of the UK population is
already immune. Nevertheless, even though it’s only one of the scenarios that
Gupta’s model implies, the fact that it seemed to contradict Ferguson’s work at
Imperial College was enough for commentators and some media to tell a story of
conflicting (and therefore untrustworthy) models.
To be clear, it does seem like some
number of people, possibly a large number, transmit the virus without being
diagnosed. They’re House Infected posing as House Susceptible. That seems clear
from researchers showing that in January, the strict travel measures that China
imposed on people trying to leave Wuhan, the center of the outbreak, slowed the spread of the disease. That shows up in location data drawn
from mobile phone apps; the extent of the disease followed people’s travel
patterns. When Wuhan locked down, that spread almost completely stopped. It bought time
for the world to prepare—which large parts of the world, including the United
States, squandered.
One clever study even tried to use a
complicated model to estimate exactly how many undiagnosed, or “undocumented,”
cases the Chinese population had in January. The researchers—some at Imperial
College and others in the US—broke the infected population into two groups,
diagnosed and undiagnosed. (Or, in the language of this particular study,
“documented” and “undocumented.”) Through surveys and an app-based data
collection project, the US team had actual data on how many people were
wandering around at any given time with a respiratory virus; their calculations
see that number peaking at more than 10 percent.
Using that as a kind of baseline, and combining it with
location data for travel among 375 Chinese cities including Wuhan, the
researchers tried various models to infer, given the number of overall
infections and where they happened, how many undiagnosed infections had to have
been out there. Their conclusion was stunning. Just 14 percent of infections
were diagnosed, they wrote. Fully 86 percent of infected people were the
walking ill, stealth transmitters of the virus. “Those undocumented infected
people were about half as infectious. However, because there are many more of
them, they are the dominant driver of the outbreak,” says one of the creators of
the model, Jeffrey Shaman, director of the Climate and Health Program at the
Columbia University School of Public Health. “This virus needs these
undocumented cases to successfully move through a society. If you’re
identifying cases, you’re going to have a better handle on it.”
For now, there’s really only one way to figure that out: tests.
Specifically, that means gathering blood from
infected people, which would contain their antibodies to the virus. That’s what
Gupta has concluded, too. “Our motivation was to explore, conceptually, the
extreme variation that could be underlying what we see,” says Gupta. “What we
see is just the tip of the iceberg. And the only way to see under the surface
is to go out there and look for these antibodies.”
One of her collaborators has already developed one of these so-called serological tests,
which can tell scientists who has been exposed to the virus and therefore now
has immunity. Over the weekend, the researchers began testing blood samples
collected from healthy Brits over the previous two months. (Gupta declined to
say where the samples came from, citing confidentiality.) She says it could be
just a matter of weeks before they have a much better picture of how vulnerable
the UK population really is. But until then, at least in the UK, it will
continue to be a tale of two models.
ONE THING THAT models will try to predict next, given the tranche of
new data that these first 80,000-plus cases in the US will yield, is when
social distancing and shelter-in-place measures can end. “You don’t make the
timeline. The virus makes the timeline,” Anthony Fauci, head of the National
Institute of Allergy and Infectious Diseases told CNN. But how
will anyone determine that timeline? The role of children as spreaders of the
disease still isn’t clear. Nor is the role of adults with mild symptoms.
“One of the crucial things that has to
happen is serology tests, testing large numbers of the population for antibodies,”
Jenkins says. This data would show whether a person has been infected—whether
or not they had symptoms. Population wide, that can give you more certainty on
the numbers of susceptible and recovered people. The tests really only work a
week or so after a person contracts the virus, but that’s valuable enough that
the UK has purchased 3.5 million blood tests already, and researchers in the
Netherlands have begun testing blood bank donations.
“The modeling is going to be absolutely key for how we
come out of this and what things look like longer-term. But for places in
Western Europe and the United States, the scientific evidence is not
particularly complicated. If you act soon, with a firmer response, you limit
early deaths and get through the initial phase more quickly,” Jenkins says.
That’s based less on models, she says, and more on research like a 2007 article about the
“non-pharmaceutical interventions” different US cities used to respond to the
1918 influenza pandemic. Places that locked down more quickly had death rates
half as high as ones that waited—and one of the coauthors of that study was the
same Marc Lipsitch who tangled with the White House last week.
That paper’s a lot clearer than a model, especially when so much about
Covid-19 remains unknown. “One of the big dangers of modeling is that a model
can get quite complicated quite quickly,” Jenkins says. “And it’s only as good
as the data that go into it.”
That’s not the only barrier, either. The channels for model-makers to get
to policymakers aren’t clear. Even though governors across the US are touting
models to justify their shelter-in-place orders and build-outs of critical care
beds, it’s just as easy for political leaders like Birx, a representative of
the federal coronavirus response, to dismiss them—as a report from
the Center for Health Security at Johns Hopkins points out.
Just as happens with models of climate change,
the presentation of a range of possible futures in epidemiological models
provides a lever for political opposition. Conservative commentators and allies
of President Trump, The Washington Post reports, are
increasingly describing social distancing orders and pleas for medical aid as
part of a deep state plot. On his radio show Friday, ultraconservative
provocateur Rush Limbaugh said,“We didn't
elect a president to defer to a bunch of health experts that we don't know. And
how do we know they're even health experts?”
The president seems to share these sentiments. After Governor Andrew
Cuomo based a request for tens of thousands of ventilators on model
projections, the president told television
personality Sean Hannity, “I don't believe you need 40,000 or 30,000
ventilators.” He based that opinion, he said, on “a feeling.” Or maybe the
president doesn’t feel
this way; two days later he tweeted that
Ford and GM should start mass-producing ventilators, and he obliquely
threatened to invoke the law he could use to make this happen. Maybe.
LISA BRANDENBURG
THOUGH, would take the models over a feeling any day.
University of Washington Medicine hospitals got their first confirmed Covid-19
patient on March 7. Three days later, she reached out to Murray and the
Institute for Health Metrics and Evaluation. The following Tuesday, his team
delivered a first round of projections for three scenarios: best, middle, and
worst case. The worst case was bad. According to the models, UWM would have to
accommodate an additional 950 Covid patients per day during the surge, which was
due to arrive April 7. With around 1,500 beds across four hospitals, they would
be overrun.
So Brandenburg’s team got to work. They had already begun
ordering more masks, gloves, face shields, and ventilators. They had opened a
drive-thru testing station. Now they moved to deploy a surge plan, canceling
all elective surgeries and trying to clear out as many beds as possible. They
built wedding-party-size triage tents outside hospitals’ emergency room
departments to keep potential Covid-19 patients away from others seeking care.
They called up ICU nurses who’d retired within the past five years. They began
shuffling in nurses, respiratory therapists, and technicians from other
departments, training them up on the specifics of critical care.
Murray’s team embedded with Brandenburg’s, providing them with daily
updated projections as new data came in. In the most recent models from late
last week, things started to take a turn—and for the first time, they were for
the better. The curve is flattening out. In the new worst-case scenario, the
number of patients has dropped by 20 percent compared with IHME’s first report.
The peak is now 10 days later, on April 17.
Across UWM’s four hospitals, Covid-19 cases are down too. At the end of
last week, doctors and nurses were caring for just over 60 Covid-19 patients,
down from about 75 a few days prior. “It looks like the social distancing is
helping,” says Brandenburg.
She knows the projections can change any time, for the worse. And that’s
still what they’re preparing for. But for the first time, she says, looking at
those graphs she allowed herself to think that maybe, maybe, it was no longer a question of
when Seattle would become the next Spain, the next Italy, the next New York.
“For the first time I feel like, ‘OK, maybe we are actually going to have all
our plans in place,’” she says.
Other hospital administrators and local public health officials should
take note. After word got out about the work IHME was doing for hospitals in
Seattle, other health care providers around the US began sending Murray emails,
asking for help with their own preparedness plans. As the individual requests
piled up, his team decided to go public with
their work last week, providing interactive state-by-state projections for how
the nation’s supply of hospital beds, intensive care units, and ventilators
will hold up over the next few months.
The new coronavirus will spread through different regions at different
speeds—depending on population density, transit patterns, and how well people
are adhering to whatever social distancing measures are in place. So Murray’s
hope is that local policymakers can use the models to get a more fine-grained
view on when their particular wave might be cresting. “We want to help them
figure out when their worst week will be and to prepare accordingly, however
they can,” says Murray. His team plans to update their models every Monday,
pulling in the latest death counts and adjusting for any changes to statewide
social distancing policies. It’s still too soon to say if Washington will be a
success story. But at least right now, it appears to be an outlier.
According to IHME’s models, 41 states will need more hospital beds than
they currently have. Twelve states will need to boost their numbers of ICU beds
by 50 percent or more. The models predict that over the next four months, these
shortfalls will contribute to the deaths of 81,000 Americans, with the number
of deaths per day peaking as soon as mid-April.
Even this estimate is generous. As epidemiologists have been pointing out on
Twitter, Murray’s models assume that states that haven’t yet enacted strict
stay-at-home orders will do so in the next week, in light of what’s happening
in New York—and that they’ll achieve Wuhan-level lockdowns,
which many public health experts are skeptical Americans
can pull off. Indeed, plenty of states, mostly conservative-leaning and where
case counts so far remain low, have resisted taking those steps. Even before
Covid-19, scientists had trouble getting policymakers to pay attention to their
warnings. Now they can’t get enough data to make those warnings specific, and
politicians are working to undermine what little the scientists are sure of.
What was already a tragedy has evolved into a disaster, reaching toward
catastrophe. And all of it was predictable.
Adam Rogers writes about
science and miscellaneous geekery. Before coming to WIRED, Rogers was a Knight
Science Journalism Fellow at MIT and a reporter for Newsweek. He is the author
of the New York Times science
bestseller Proof: The Science of
Booze.
Megan Molteni is a staff
writer at WIRED, covering biotechnology, public health, and genetic privacy. Previously, she
freelanced as a reporter, audio producer, and fact-checker. Her work has
appeared in Popular Science, Discover, Undark, Nautilus, and Aeon. She studied
biology and ultimate frisbee at Carleton College and has a graduate...
