the Future of the COVID-19 Pandemic
Disease
experts have largely focused on how we got to where we are now with
coronavirus infections. Improved data collection and sharing can enhance
projections of what’s to come.
While
politicians and the public obsess about how and when the coronavirus
pandemic will peak, the scientists able to make such projections are
struggling to get a grip on what’s happening right now.
“Sorry, not
doing any interviews at the moment so that we can fully focus on our
local and regional response,” one leading US epidemiologist wrote in an
email when contacted by
The Scientist.
Like any other
models, the projections of how the outbreak will unfold, how many people
will become infected, and how many will die, are only as reliable as
the scientific information they rest on. And most modelers’ efforts so
far have focused on improving these data, rather than making premature
predictions.
“Most of the work that modelers have done recently or
in the first part of the epidemic hasn’t really been coming up with
models and predictions, which is I think how most people think of it,”
says
John Edmunds,
who works in the Centre for the Mathematical Modelling of Infectious
Diseases at the London School of Hygiene & Tropical Medicine.
“Most
of the work has really been around characterizing the epidemiology,
trying to estimate key parameters. I don’t really class that as modeling
but it tends to be the modelers that do it.”
We can slow it down by canceling all these events, which we completely should do. But it’s still going to spread to most places.
—Maciej Boni, Penn State University
These
variables include key numbers such as the disease incubation period,
how quickly the virus spreads through the population, and, perhaps most
contentiously, the case-fatality ratio. This sounds simple: it’s the
proportion of infected people who die. But working it out is much
trickier than it looks.
“The non-specialists do this all the time and
they always get it wrong,” Edmunds says.
“If you just divide the total
numbers of deaths by the total numbers of cases, you’re going to get the
wrong answer.”
Earlier this month, Tedros Adhanom Ghebreyesus,
the head of the World Health Organization, dismayed disease modelers
when he said COVID-19 (the disease caused by the SARS-CoV-2 coronavirus)
had
killed 3.4 percent
of reported cases, and that this was more severe than seasonal flu,
which has a death rate of around 0.1 percent. Such a simple calculation
does not account for the two to three weeks it usually takes someone who
catches the virus to die, for example. And it assumes that reported
cases are an accurate reflection of how many people are infected, when
the true number will be much higher and the true mortality rate much
lower.
Edmunds calls this kind of work
“outbreak analytics” rather
than true modeling, and he says the results of various specialist
groups around the world are starting to converge on COVID-19’s true
case-fatality ratio, which seems to be about 1 percent.
Once such
numbers are pinned down, then modelers can move onto what’s called
“situational awareness,” Edmunds explains. Much of that work looks
backward, asking how many cases there might have been in a specific
location a few weeks ago and using that information to work out how it
could have spread since.
Deaths are the most useful data points
for these analyses. For example, if modelers assume a case-fatality
ratio of 1 percent, and that it usually takes 15 days for an infected
person to die, then they know a death reported today in a specific
region means that 100 people were likely infected there 15 days ago. Add
in the time it takes cases to double—Edmunds says it seems to take five
days—then modelers can estimate that over those 15 days the number of
cases swelled to 800. So, for every death in a region, that means about
800 others are already infected, most of whom will not have been
identified. This pattern was verified in Italy, Edmunds says, which as
of today has reported
12,462 cases and 827 deaths.
When officials tested people living near where someone had died from
the disease, in many cases they found hundreds of others were already
carrying the virus.
Maciej Boni,
a biologist at Penn State University who has studied the spread of
influenza in the tropics, says this high number of undetected cases
means the spread of the virus can’t be tracked from the numbers of
confirmed infections.
“At this point, the spread is a moot point,” says
Boni. “We can slow it down by canceling all these events, which we
completely should do. But it’s still going to spread to most places.”
Left
unchecked, infectious outbreaks typically plateau and then start to
decline when the disease runs out of available hosts. But it’s almost
impossible to make any sensible projection right now about when that
will be, Boni says, or about how many people will ultimately be
affected. Modelers can try, but to do so they need much better
information, such as how many people infected show natural immunity.
Most
of these forward-looking “scenario planning” models currently assume
everyone on the planet is susceptible, Edmunds says. Only better
surveillance and data, in particular, from serum tests that would
indicate whether people have been exposed to the virus whether or not
they developed symptoms, will make those calculations more realistic.
“At the moment, we’ve got no data to tie that model down. But as the
epidemic proceeds and everytime more data comes out, like every day or
every week, we refit the model and then we redo our projections.”
To
build better models, some disease experts argue that the world needs to
improve the way such data are handled and made available. In an
editorial published this week in
Science Translational Medicine,
Scott Layne,
an epidemiologist at the University of California, Los Angeles, School
of Public Health, and his colleagues propose a new data bank be created
in which researchers can share results on, for example, how much virus
is shed by infected people and
when that starts.
“We’re
all in the process of collecting that information. What this effort
would do is, as that data comes in, it would point to it and help to
organize it,” Layne tells
The Scientist.
Backed by better
information, models could help determine policies to control spread, he
adds.
“If those models do have any validity, then you can perturb them
or pressure test them against various sorts of interventions, whether
it’s making people move less or cutting down contact by a certain
percentage.”
According to
Reuters,
Chinese officials say the restrictions on travel they put in place have
pushed the epidemic to peak in China. Zhong Nanshan, the Chinese
government’s senior medical adviser, claimed at a
press conference
this week that if other nations follow China’s lead, then the pandemic
could be tamed within months.
“My advice is calling for all countries to
follow WHO instructions and intervene on a national scale,” he says.
“If all countries could get mobilized, it could be over by June.”
David Adam is a UK-based freelance journalist. Email him at davidneiladam@gmail.com and follow him on Twitter @davidneiladam.