Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.

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for
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or
distribu2on
Modeling
the
Ebola
Outbreak
in
West
Africa,
2014
February
24th
Update
Bryan
Lewis
PhD,
MPH
(blewis@vbi.vt.edu)
presen2ng
on
behalf
of
the
Ebola
Response
Team
of
Network
Dynamics
and
Simula2on
Science
Lab
from
the
Virginia
Bioinforma2cs
Ins2tute
at
Virginia
Tech
Technical
Report
#15-­‐016

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distribu2on
NDSSL
Ebola
Response
Team
Staff:
Abhijin
Adiga,
Kathy
Alexander,
Chris
Barre.,
Richard
Beckman,
Keith
Bisset,
Jiangzhuo
Chen,
Youngyoun
Chungbaek,
Stephen
Eubank,
Sandeep
Gupta,
Maleq
Khan,
Chris
Kuhlman,
Eric
Lofgren,
Bryan
Lewis,
Achla
Marathe,
Madhav
Marathe,
Henning
Mortveit,
Eric
Nordberg,
Paula
Stretz,
Samarth
Swarup,
Meredith
Wilson,Mandy
Wilson,
and
Dawen
Xie,
with
support
from
Ginger
Stewart,
Maureen
Lawrence-­‐Kuether,
Kayla
Tyler,
Bill
Marmagas
Students:
S.M.
Arifuzzaman,
Aditya
Agashe,
Vivek
Akupatni,
Caitlin
Rivers,
Pyrros
Telionis,
Jessie
Gunter,
Elizabeth
Musser,
James
Schli.,
Youssef
Jemia,
Margaret
Carolan,
Bryan
Kaperick,
Warner
Rose,
Kara
Harrison
2

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distribu2on
Currently
used
data
(as
of
Feb
18th,
2014)
● Data
from
WHO,
MoH
Liberia,
and
MoH
Sierra
Leone,
available
at
h.ps://github.com/cmrivers/ebola
● MoH
and
WHO
have
reasonable
agreement
● Sierra
Leone
case
counts
censored
up
to
4/30/14.
● Time
series
was
filled
in
with
missing
dates,
and
case
counts
were
interpolated.
3
Cases
Deaths
Guinea
3,108
2,057
Liberia
9,007
3,900
Sierra
Leone
11,103
3,408
Total
23,253
9,380

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distribu2on
Liberia
infec2on
rate
4

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distribu2on
Liberia
Forecast
5
1/13
-­‐
1/19
1/20
-­‐
1/26
1/27
-­‐
2/02
2/03
-­‐
2/08
2/09
-­‐
2/16
2/17
-­‐
2/23
2/24
-­‐
3/02
Reported
3
5
3
3
0
Updated
model
1
6
4
2
7
4
2
Reproduc2ve
Number
Community
0.3
Hospital
0.3
Funeral
0.2
Overall
0.8

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distribu2on
Liberia-­‐
Prevalence
6
Date
People
in
H
+
I
2/2
21
2/9
13
2/16
7
2/23
4
3/02
1
3/09
1

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or
distribu2on
Sierra
Leone
infec2on
rate
7

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or
distribu2on
Sierra
Leone
Forecast
8
35%
of
cases
are
hospitalized
ReproducNve
Number
Community
0.7
Hospital
0.2
Funeral
0.1
Overall
1.0
1/13
-­‐
1/19
1/20
-­‐
1/26
1/27
-­‐
2/02
2/03
-­‐
2/08
2/09
-­‐
2/16
2/17
-­‐
2/23
2/24
-­‐
3/02
Reported
84
56
30
51
42
Updated
model
81
67
55
44
36
30
24

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distribu2on
Sierra
Leone-­‐
Prevalence
9
Date
People
in
H
+
I
2/2
112
2/9
92
2/16
76
2/23
63
3/02
51
3/09
42

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distribu2on
Guinea
Forecasts
10
40%
of
cases
are
hospitalized
ReproducNve
Number
Community
0.25
Hospital
0.09
Funeral
0.01
Overall
0.36
1/13
-­‐
1/19
1/20
-­‐
1/26
1/27
-­‐
2/02
2/03
-­‐
2/08
2/09
-­‐
2/16
2/17
-­‐
2/23
2/24
-­‐
3/02
Reported
32
13
16
14
65
Updated
model
37
27
18*
12*
5*
4*
2*
*
too
small
for
reliable
forecas2ng

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distribu2on
Guinea
Prevalence
11
Date
People
in
H+I
2/2
53
2/9
35
2/16
23
2/23
15
3/02
10
3/09
6

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Compartmental
Model
Forecasts
• Development
of
more
nimble
stochas2c
but
compartmental
models
for
forecas2ng
• Dynamic
manipula2on
of
behavioral
changes
poses
a
challenge
for
most
solvers,
but
techniques
exist
– S2ll
working
out
the
kinks
12
“Cones
of
uncertainty”

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distribu2on
Update
on
Vaccine
Trials
• Advantages
– Focuses
energy
on
contact
tracing
which
is
appropriate
without
a
vaccine
trial
going
on
– Es2mated
that
a
dozen
or
so
rings
like
this
may
provide
sufficient
power
for
reasonable
efficacy
es2mates
13
Suscep2ble
Infected
Vaccinate
now
Vaccinate
later
• New
hybrid
trail
design
to
be
tried
– Not
stepped
wedge
but
a
cluster-­‐based
approach
that
randomizes
when
vax
is
received
by
contacts
– Timing:
immediate
or
21
days
– Star2ng
next
week
in
Guinea

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distribu2on
Early
vaccina2on
study
ObjecNve:
Assuming
that
early
deployment
of
vaccine
interven2ons,
what
vaccine
stockpile
is
necessary
to
control
a
reasonably
stressful
outbreak
like
the
2014
West
African
outbreak?
14

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Early
Interven2on
Design
Disease:
Calibrated
to
Liberia
(explosive
growth)
IntervenNon
sweep:
every
2
weeks,
June
thru
July
Doses:
1k,
2k,
5k,
10k
Case
finding:
100%,
80%,
50%
Compliance:
25%,
50%,
75%
Ring
VaccinaNon:
Assume
all
contacts
can
be
found
(thus
100%
compliance
means
all
contacts
have
been
found
and
vaccinated)
Efficacy:
50%,
80%
15

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APP
UPDATE
Eyes-­‐on-­‐the-­‐Ground
16

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Eyes-­‐on-­‐the-­‐Ground:
Overview
Road
condi2ons
in
West
Africa
can
vary
day
to
day.
Eyes
on
the
Ground
allows
witnesses
on
the
ground
to
record
what
they
see
through
an
easy-­‐to-­‐use
web
interface.

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Eyes-­‐on-­‐the-­‐Ground:
New
Features
• Added
Sierra
Leone
– Ini2alized
with
GIS
calculated
es2mates
of
travel
2me
• Query-­‐able
interface
added
– Summary
of
previous
responses
instantly
available
on
main
screen
upon
origin-­‐des2na2on
selec2on
• GUI
improvements
and
browser
support
18

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Eyes-­‐on-­‐the-­‐Ground:
New
Features
Step
1:
Select
a
country.
Step
2:
Enter
the
details
of
your
report.
No2ce
that
you
can
see
when
the
route
was
last
reported
on,
what
the
last
user
reported,
and
the
mean
travel
dura2on
for
the
last
5
trips.

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for
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or
distribu2on
Eyes-­‐on-­‐the-­‐Ground:
New
Features
Travelers
can
view
past
reports
from
the
website
in
order
to
plan
the
best
route
to
medical
help
or
for
delivering
supplies.
h.p://ebola.vbi.vt.edu/eyesontheground/