By Kari Lydersen
in Discover Magazine
The Ebola virus has consistently
stayed several steps ahead of doctors, public officials and others trying to
fight the epidemic. Throughout the first half of 2014, it spread quickly as
international and even local leaders failed to recognize the severity of the
situation. In recent weeks, with international response in high gear, the virus
has thrown more curve balls.
The spread has significantly slowed in Liberia and beds for Ebola patients are
empty even as the U.S. is building multiple treatment centers there. Meanwhile
the epidemic has escalated greatly in Sierra Leone, which has a serious dearth of treatment centers. And in Mali,
where an incursion was successfully contained in October, a rash of new cases has spread from an infected
imam.
Predicting the trajectory of Ebola
rather than playing catching-up could do much to help prevent and contain the
disease. Some experts have called for prioritizing mobile treatment units that
can be quickly relocated to the spots most needed. Figuring out where Ebola is
likely to strike next or finding emerging hot spots early on would be key to
the placement of these treatment centers.
But such modeling requires data, and
lots of it. And for stressed healthcare workers on the ground and government
and non-profit agencies scrambling to combat a raging epidemic, collecting and
disseminating data is often not a high priority.
Air traffic connections from West
African countries to the rest of the world. Guinea, Liberia, and Sierra Leone
are not well connected outside the region; Nigeria, in contrast, is.
Population
Flows
The crux to combating Ebola is
understanding how people move between different cities, villages and countries.
Such data are already captured in a variety of metrics. On the macro level,
records of border crossings and airline flights create clear pictures. On a
more local level, trucking and bus routes and traffic flows help. But
especially in rural areas like the forests of Guinea where the epidemic
started, even more detailed information is needed.
Alessandro Vespignani is one researcher trying to
gather that information. Vespignani seeks population data at the most granular
level possible, trying to determine numbers of people and types of dwellings
within five by five mile boxes, for example. He uses local census numbers plus
data from the LandScan program out of Oak Ridge National
Laboratory and Worldpop,
a UK-based project to map populations in Africa, Asia and Latin America with a
focus on development and health. He integrates that information with Ebola data
provided by health agencies, and with data on the movement of people from
airlines, borders, transportation records and other sources. In early September
he published a high-profile projection of the potential
international spread of Ebola, using these data sources.
Mapping
with Mobile Phones
Mobile phone records are another
promising way to track the movements of people at a more localized level. Phone
data stripped of users’ identities helped researchers understand past epidemics
including the cholera outbreak following the 2010
Haiti earthquake.
Researchers involved in the Swedish
non-profit organization Flowminder have been trying to use mobile phone
records to shed light on Ebola’s spread. However they’ve been so
far stymied by a lack of cooperation from phone companies and from government
regulators in West Africa, who have not made the data available. To demonstrate
the potential of its approach, Flowminder created a model showing people’s movements in Senegal and
Ivory Coast using several-year-old data from Orange Telecom. Flowminder board
member Andy Tatem, a Reader at the University of Southampton, says that
negotiations are ongoing with government regulators and companies which could
provide the mobile phone data, but it has been slow going.
“This kind of data can give you
information about population level movements, how they change over time, how
they change over space,” says Tatem, who is also director of Worldpop and an
expert in modeling population movements related to malaria. (There is no talk
of using phone data to track individual people infected by Ebola. That would be
considered a major breach of privacy, and would also likely be impossible given
the high number of cases.)
“It’s an area of the world where
there are huge seasonal movements, mobility patterns changing month by month.
People cross borders between countries, people are moving to the cities looking
for alternative work.”
And unlike conventional disease
surveillance, Tatem notes, once companies and regulators give the green light,
using mobile phone data is basically “free” and involves no continuous action
from companies and regulators.
Data
Gaps
However even mobile phone data isn’t
a perfect relay of what’s going on on the ground. A dense city could have
numerous phone towers, allowing fairly precise modeling of human flows, even
down to specific neighborhoods. But in rural areas, one tower might cover a
radius of 50 miles or more, making it less useful in estimating movements
between small villages.
And in the impoverished and often
geographically isolated areas decimated by Ebola, many people don’t have mobile phones. As a whole,
mobile phone usage in Africa is high and growing. But Guinea, Sierra Leone and
Liberia have among the lowest mobile phone usage rates in Sub-Saharan Africa,
with between 51 and 54 percent of households having phones, compared to 78
percent for Nigeria and 96 percent for Mauritania, according to a Gallup poll
this year. In the U.S., nine in 10 adults have a mobile phone.
Capturing
Clinical Data
Using data for a dynamic
understanding of the epidemic also naturally involves information compiled by
health care workers on the ground, including counts of cases, deaths and people
in isolation. Record-keeping during the epidemic has been notoriously flawed
and incomplete, and getting records from far-flung clinics that may not have
computers is a daunting undertaking.
“We are talking about very weak
health care systems in the region that were right away overwhelmed by the
situation,” says Vespignani. “In that case you cannot ask people who are really
struggling to save lives, to get data.”
But Vespignani is hopeful that as
the epidemic ebbs, as it has at least in Liberia, staff and officials will be
able to focus more on compiling and providing data from Ebola treatment
centers.
For instance, researchers could
compare predicted caseloads with actual caseloads in specific areas where
initiatives around safe burials, prevention, isolation or other best practices
have been implemented. That could reveal whether such efforts have
significantly reduced infections compared to what otherwise would have been
expected. Another aspect not currently captured in models, he says, is “social
behavior” like community perception of the disease and rate of compliance
with government and agency warnings.
And such data could be key to making
sure the epidemic is really stamped out, and doesn’t resurface catching people
unaware.
“When I hear people saying the
epidemic is subsiding I always shiver,” he says. “We see improvement, there’s a
slowing down and things are improving in certain places, but we need to have
the last battle to really try to contain it. Any decrease of effort, any
arrogance to say we are good, could really backfire and we could find ourselves
with a disaster.”
No comments:
Post a Comment