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.

1. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Modeling
the
Ebola
Outbreak
in
West
Africa,
2014
February
10th
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-­‐015

2. DRAFT
–
Not
for
a.ribu2on
or
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

3. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Currently
Used
Data
(as
of
Feb
4th,
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
2,975
1,944
Liberia
8,745
3,746
Sierra
Leone
10,740
3,276
Total
22,724
8,981

4. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Liberia
–
Case
Loca2ons
4

5. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Liberia
infec2on
rate
5

6. DRAFT
–
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for
a.ribu2on
or
distribu2on
Liberia
Forecast
6
12/23
-­‐
1/01
1/02
-­‐
1/08
1/09
-­‐
1/15
01/16
-­‐
1/22
1/23
-­‐
2/01
2/02
-­‐
2/08
2/09
-­‐
2/16
Reported
190
163
107
130
197
Updated
model
187
174
162
151
141
131
122
Reproduc2ve
Number
Community
0.3
Hospital
0.3
Funeral
0.2
Overall
0.8

7. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Liberia
long
term
forecasts
7
Date
Weekly
forecast
2/9
122
2/16
114
2/23
106
3/02
99
3/09
92
3/16
86
3/23
80

8. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Liberia-­‐
Prevalence
8
Date
People
in
H
+
I
2/2
331
2/9
308
2/16
288
2/23
268
3/02
250
3/09
233

9. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Sierra
Leone
infec2on
rate
9

10. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Sierra
Leone
Forecast
10
35%
of
cases
are
hospitalized
ReproducRve
Number
Community
0.7
Hospital
0.2
Funeral
0.1
Overall
1.0
12/28
-­‐
1/04
1/05
-­‐
1/11
1/12
-­‐
1/18
1/19
-­‐
1/25
1/26
-­‐
2/01
2/02
-­‐
2/08
02/09
-­‐
02/16
Reported
334
260
212
129
146
Updated
model
317
290
267
244
224
205
188

11. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
SL
longer
term
forecast
11
Sierra
Leone
–
Newer
Model
fit
–
Weekly
Incidence
Date
Weekly
forecast
2/2
224
2/9
205
2/16
188
2/23
172
3/02
158
3/09
145
3/16
132

12. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Sierra
Leone
-­‐
Prevalence
12
Date
People
in
H
+
I
1/26
448
2/2
411
2/9
376
2/16
345
2/23
316
3/02
289
3/09
265

13. DRAFT
–
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for
a.ribu2on
or
distribu2on
Guinea
Forecasts
13
40%
of
cases
are
hospitalized
ReproducRve
Number
Community
0.25
Hospital
0.09
Funeral
0.01
Overall
0.36
12/22
-­‐
12/28
12/29
-­‐
1/04
1/05
-­‐
1/11
1/12
-­‐
1/18
1/19
-­‐
1/25
1/26
-­‐
2/01
2/02
-­‐
2/08
2/09
-­‐
2/15
Reported
100
45
30
46
44
38
Updated
model
94
91
77
61
45
33
24
18

14. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Guinea
–
longer
term
forecast
14
Date
Weekly
forecast
1/26
45
2/2
33
2/9
24
2/16
18*
2/23
13*
3/02
9*
*
too
small
for
reliable
forecas2ng

15. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Guinea
Prevalence
15
Date
People
in
H+I
1/26
95
2/2
93
2/9
90
2/16
88
2/23
86
3/02
83
3/09
81

16. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Agent-­‐based
Model
Progress
• Review:
Sensi2vity
to
compliance
levels
vaccine
campaign
study
• Review:
Stepped-­‐Wedge
study
design
being
considered
by
CDC
details
from
Ebola
Modeling
conference
• Review:
Analy2c
methods
developed
for
comparison
of
stochas2c
simula2on
results
• Update:
Calibra2on
for
SL
updated
• Update:
Study
design
for
future
outbreak
planning
• Ongoing:
Stochas2c
ex2nc2on
/
2me
to
zero
16

17. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Calibra2on
of
Simulated
Vaccine
Campaigns
17
0
5000
10000
15000
20000
25000
55
62
69
76
83
90
97
104
111
118
125
132
139
146
153
160
167
174
181
188
195
202
209
216
223
230
237
244
251
258
265
272
279
286
293
300
307
314
321
328
335
342
349
356
363
370
Model
80%e
30%c
Model
80%e
90%c
Model
50%e
30%c
Model
50%e
90%c
MoH
Data

18. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
18
%
Change
in
Infec2ons
Following
Vaccina2on
Beginning
Feb
1
(30k
Doses)
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Baseline
-­‐
replicate
11
80e_30c
-­‐
replicate
15
80e_50c
-­‐
replicate
2
80e_70c
-­‐
replicate
2
80e_90c
-­‐
replicate
20
50e_30c
-­‐
replicate
12
50e_50c
-­‐
replicate
15
50e_70c
-­‐
replicate
18
50e_90c
-­‐
replicate
13

19. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
19
30k
Doses
–
Percent
Reduc2on
by
Efficacy
and
Compliance
Compliance
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
90%
70%
50%
30%
80%
Efficacy
50%
Efficacy

20. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
20
30k
Doses
-­‐
Cumula2ve
Infec2ons
using
the
Mean
of
most
relevant
replicates
%
InfecRons
Occurring
Between
Feb-­‐1
and
Apr-­‐1
%
ReducRon
Compliance
80%
Efficacy
50%
Efficacy
80%
Efficacy
50%
Efficacy
90%
27.54%
32.38%
30.55%
18.34%
70%
31.22%
34.78%
21.25%
12.28%
50%
32.62%
35.07%
17.73%
11.54%
30%
34.88%
35.83%
12.03%
9.62%
Baseline
39.65%

21. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
21
Compliance
300k
Doses
–
Percent
Reduc2on
by
Efficacy
and
Compliance
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
90%
70%
50%
30%
80%
Efficacy
50%
Efficacy

22. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
22
300k
Doses
-­‐
Cumula2ve
Infec2ons
using
the
Mean
of
most
relevant
replicates
%
InfecRons
Occurring
Between
Feb-­‐1
and
Apr-­‐1
%
ReducRon
in
Cases
A[er
Feb-­‐1
Compliance
80%
Efficacy
50%
Efficacy
80%
Efficacy
50%
Efficacy
90%
26.47%
30.29%
33.23%
23.59%
70%
29.61%
32.34%
25.33%
18.42%
50%
31.04%
32.41%
21.71%
18.24%
30%
32.31%
35.31%
18.49%
10.93%
Baseline
39.65%

23. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Vaccine
Trial
Design
• Stepped
wedge:
Enroll
and
follow-­‐up
all,
vaccinate
over
2me,
compare
rates
vax
and
no-­‐vax
cohorts
23
Weeks
a[er
start
of
trail
Cluster
doses
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
1
~333
2
~333
3
~333
4
~333
5
~333
6
~333
7
~333
8
~333
9
~333
10
~333
11
~333
12
~333
13
~333
14
~333
15
~333
16
~333
17
~333
18
~333
Vaccinated
but
not
seroconverted
Compare
rates
among
enrolled
but
not
vaccinated
vs.
seroconverted
vaccinees
Vaccinated
and
protected
Enrolled
but
not
vaccinated
Blue
box
follow
up
2me
for
analysis
of
efficacy

24. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Stepped
Wedge
Design
• Key
components
– Assume
weeks
have
similar
hazard
of
infec2on
across
clusters
(or
classes
of
clusters)
– Cox
Propor2onal
Hazards
Risk
can
be
used
to
assess
efficacy
• Under
considera2on
for
CDC-­‐run
trial
– Current
assessment
is
its
too
underpowered,
when
there
is
declining
incidence
– Leaning
towards
a
different
cluster
based
design
24

25. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Stochas2c
Simula2ons
• CNIMS
simula2ons
include
a
lot
structure
to
capture
the
inherent
stochas2city
of
the
real
world
25
Distribu2on
of
1000
replicates
of
Liberian
Ebola
epidemics

26. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Stochas2c
Simula2ons
• Capturing
this
fundamental
behavior
of
complex
systems
is
important
– Used
to
es2mate
bounds
on
“possible
worlds”
– Provides
rich
distribu2ons
of
outcomes
from
interven2ons
for
sta2s2cal
analysis
• Need
to
apply
different
techniques
for
analysis
– Ques2ons
about
the
outcome
of
ac2ons
given
the
system
is
in
par2cular
state
requires
iden2fica2on
of
individual
realiza2ons
of
the
simula2on
that
fit
“criteria”
or
combines
them
appropriately
– Example:
Given
we
have
an
outbreak
like
what
has
happened
in
Sierra
Leone
(to
the
degree
we’ve
been
able
to
observe
it
accurately)
what
would
a
vaccine
campaign
do?
• Filter
realiza2ons
most
like
observed
data
• Discount
26

27. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Stochas2c
Simula2ons
• Bayesian
approach,
analyze
all
replicates,
consider
how
well
observed
fits
in,
use
this
to
es2mate
uncertainty
and
assign
weights
for
outcome
analysis
27

28. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Agent-­‐based
Calibra2on
• Updated
for
Sierra
Leone
28

29. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Incorpora2ng
Uncertainty
29
• Different
“fi.ed”
parameter
sets
yield
different
levels
of
stochas2c
variance
• Different
“fi.ed”
parameter
sets
yield
different
levels
of
stochas2c
variance

30. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Ebola
Outbreak
Planning
• What
levels
of
vaccine
are
needed
and
when
to
prevent
future
outbreaks?
• Assump2ons
– One
of
the
vaccine
candidates
will
be
effec2ve
and
safe
enough
to
be
used
– Current
outbreak
is
a
“worst
case”
– Ini2al
control
is
a.empted
with
classic
isola2on
and
treatment
30

31. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Planning
Study
Design
• Scenario:
– Use
models
fit
to
current
outbreaks
in
all
3
countries
• Interven2ons:
– Vaccine
doses:
April
1K,
July
30K
(more
/
less?)
• Metrics:
– How
much
is
needed
to
stop
outbreak
– Explore
sensi2vi2es
using
a
ring-­‐vaccina2on
strategy
• Case
iden2fica2on
• Efficacy
of
vaccine
• Contact
finding
/
compliance
31

32. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
32
• Allows
users
to
compare
and
filter
on
mul2ple
epicurves
• Visualizes
both
incidence
data
and
cumula2ve
data
along
with
uncertainty
bounds
Compare
forecasts
in
EpiViewer

33. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
EpiViewer
–
data
filter
and
features
33
• Easy
upload
and
download
mechanic
for
acquiring
and
adding
plot
data
• Data
filter
plots
epicurves
based
on:
region,
category
of
curves,
surveillance
data,
forecasts,
model
output,
name
of
the
curves
• Zoomable
date
selec2on
for
specific
ranges
on
concurrent
plots

34. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
EpiViewer
–
Animated
plots
• Added
anima2on
mode
for
be.er
visual
comparison
34