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
3rd
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-­‐014

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
Jan
23rd,
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
2,871
1,876
Liberia
8,478
3,605
Sierra
Leone
10,340
3,145
Total
21,724
8,641

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
–
Not
for
a.ribu2on
or
distribu2on
Liberia
Forecast
6
12/16
-­‐
12/22
12/23
-­‐
1/01
1/02
-­‐
1/08
1/09
-­‐
1/15
01/16
-­‐
1/22
1/23
-­‐
2/01
2/02
-­‐
2/08
Reported
100
190
163
107
130
Newer
model
200
187
174
162
151
141
131
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
2/2
131
2/9
122
2/16
114
2/23
106
3/02
99
3/09
92
3/16
86

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
12/21
-­‐
12/27
12/28
-­‐
1/04
1/05
-­‐
1/11
1/12
-­‐
1/18
1/19
-­‐
1/25
1/26
-­‐
2/01
2/02
-­‐
2/08
Reported
405
334
491
268
Newer
model
452
439
427
414
402
391
380

11. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
SL
longer
term
forecast
11
Sierra
Leone
–
Newer
Model
fit
–
Weekly
Incidence
Date
Weekly
forecast
1/26
402
2/2
391
2/9
380
2/16
369
2/23
358
3/02
348
3/09
338

12. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Sierra
Leone
-­‐
Prevalence
12
Date
People
in
H
+
I
1/26
882
2/2
900
2/9
918
2/16
937
2/23
995
3/02
1015
3/09
1034

13. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Guinea
Forecasts
13
40%
of
cases
are
hospitalized
ReproducRve
Number
Community
0.7
Hospital
0.1
Funeral
0.1
Overall
0.9
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
Reported
166
106
62
23
Newer
model
94
91
89
86
84
82
80

14. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Guinea
–
longer
term
forecast
14
Date
Weekly
forecast
1/26
82
2/2
80
2/9
78
2/16
76
2/23
74
3/02
72

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
• Sensi2vity
to
compliance
with
vaccine
assessed
• Stepped-­‐Wedge
study
design
being
considered
by
CDC
details
from
Ebola
Modeling
conference
• Analy2c
methods
developed
for
comparison
of
stochas2c
simula2on
results
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