Nigeria a model
for quick action, scientists find
Ebola. The word brings fear of an unseen and potentially
lethal enemy. But there are ways to stop its spread, say infectious disease
scientists.
Quick intervention is needed, according to the
researchers, who recently published their findings in the journal
Eurosurveillance.
Analyzing Ebola cases
in Nigeria, a country with success in containing the disease, the
scientists estimated the rate of fatality, transmission progression,
proportion of health care workers infected, and the effect of control
interventions on the size of the epidemic.
Rapid response
needed
"Rapid control is necessary, as is demonstrated by
the Nigerian success story," says Arizona State University (ASU) scientist
Gerardo Chowell, senior author of the paper.
"This is critically important for countries in the
West Africa region that are not yet affected by the Ebola epidemic, as well as
for countries in other regions of the world that risk importation of the
disease."
The research
is funded by the U.S. National Science Foundation (NSF)-National Institutes of
Health (NIH)-Department of Agriculture (USDA) Ecology and Evolution of
Infectious Diseases (EEID) Program.
"Controlling
a deadly disease like Ebola requires understanding how it's likely to
spread, and knowing the ways of managing that spread that are most likely to be
effective," says Sam Scheiner, NSF EEID program director.
"Being able to respond quickly needs a foundation of
knowledge acquired over many years. The work of these scientists is testimony
to long-term funding by the EEID program."
Control measures
in Nigeria
The largest Ebola outbreak to date is ongoing in West
Africa, with more than 8,000 reported cases and 4,000 deaths. However, just 20
Ebola cases have been reported in Nigeria, with no new cases since early
September.
All the cases in Nigeria stem from a single traveler
returning from Liberia in July.
The study used epidemic modeling and computer simulations
to project the size of the outbreak in Nigeria if control interventions had
been implemented during various time periods after the initial case, and
estimated how many cases had been prevented by the actual early interventions.
"This timely work demonstrates how computational
simulations, informed by data from health care officials and the complex social
web of contacts and activities, can be used to develop both preparedness plans
and response scenarios," says Sylvia Spengler, program director in NSF's
Directorate for Computer and Information Science and Engineering, which also
supported the research.
Control measures implemented in Nigeria included holding
all people showing Ebola symptoms in an isolation ward if they had had contact
with the initial case. If Ebola was confirmed through testing, people diagnosed
with the disease were moved to a treatment center.
Asymptomatic individuals were separated from those
showing symptoms; those who tested negative without symptoms were discharged.
Those who tested negative but showed symptoms--fever,
vomiting, sore throat and diarrhea--were observed and discharged after 21 days
if they were then free of symptoms, while being kept apart from people who had
tested positive.
Brief window of
opportunity
Ebola transmission is dramatically influenced by how
rapidly control measures are put into place.
"Actions taken by health authorities to contain the
spread of disease sometimes can, perversely, spread it," says NSF-funded
scientist Charles Perrings, also of ASU.
"In the Nigeria case, people who tested negative but
had some of the symptoms were not put alongside others who tested
positive," says Perrings. "So they had no incentive to flee, and
their isolation did nothing to increase infection rates. Elsewhere in the
region isolation policies have had a different effect."
The researchers found that the projected effect of
control interventions in Nigeria ranged from 15-106 cases when interventions
are put in place on day 3; 20-178 cases when implemented on day 10; 23-282
cases on day 20; 60-666 cases on day 30; 39-1,599 cases on day 40; and 93-2,771
on day 50.
The person who was initially infected generated 12
secondary cases in the first generation of the disease; five secondary cases
were generated from those 12 in the second generation; and two secondary cases
in the third generation.
That leads to a rough estimate of the reproduction number
according to disease generation declining from 12 during the first generation,
to approximately 0.4 during the second and third disease generations.
A reproductive number above 1.0 indicates that the
disease has the potential to spread.
Recent estimates of the reproduction number for the
ongoing Ebola epidemic in Sierra Leone and Liberia range between 1.5 and 2 (two
new cases for each single case), indicating that the outbreak has yet to be
brought under control.
The effectiveness of the Nigerian response, scientists
say, is illustrated by a dramatic decrease in the number of secondary cases
over time.
The success story for Nigeria, they maintain, sets a
hopeful example for other countries, including the United States.
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