This document provides a summary and update of modeling work being done on the 2014 Ebola outbreak in West Africa. It includes current case and death counts by country, as well as forecasts for Liberia and Sierra Leone based on transmission models. It also discusses potential interventions like vaccinations and notes next steps such as expanding the modeling to other affected countries.

1. Modeling
the
Ebola
Outbreak
in
West
Africa,
2014
Sept
2nd
Update
Bryan
Lewis
PhD,
MPH
(blewis@vbi.vt.edu)
Caitlin
Rivers
MPH,
Eric
Lofgren
PhD,
James
Schli.,
Ka2e
Dunphy,
Stephen
Eubank
PhD,
Madhav
Marathe
PhD,
and
Chris
Barre.
PhD
Technical
Report
#14-­‐099
DRAFT
–
Not
for
a.ribu2on
or
distribu2on

2. Currently
Used
WHO
Data
Cases
Deaths
Guinea
648
430
Liberia
1378
694
Sierra
Leone
1026
422
Nigeria
17
6
Total
3069
1563
● Data
reported
by
WHO
on
Aug
29
for
cases
as
of
Aug
26
● Sierra
Leone
case
counts
censored
up
to
4/30/14.
● Time
series
was
filled
in
with
missing
dates,
and
case
counts
were
interpolated.
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
2

3. Epi
Notes
• Case
iden2fied
in
Senegal
– Guinean
student,
sought
care
in
Dakar,
iden2fied
and
quaran2ned
though
did
not
report
exposure
to
Ebola,
thus
HCWs
were
exposed.
BBC
• Liberian
HCWs
survival
credited
to
Zmapp
– Dr.
Senga
Omeonga
and
physician
assistant
Kynda
Kobbah
were
discharged
from
a
Liberian
treatment
center
on
Saturday
ader
recovering
from
the
virus,
according
to
the
World
Health
Organiza2on.
CNN
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
3

4. Epi
Notes
• Guinea
riot
in
Nzerekore
(2nd
city)
on
Aug
29
– Market
area
“disinfected,”
angry
residents
a.ack
HCW
and
hospital,
“Ebola
is
a
lie”
BBC
• India
quaran2nes
6
“high-­‐risk”
Ebola
suspects
on
Monday
in
New
Delhi
– Among
181
passengers
who
arrived
in
India
from
the
affected
western
African
countries
HealthMap
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
4

5. Further
evidence
of
endemic
Ebola
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
5
• 1985
manuscript
finds
~13%
sero-­‐prevalence
of
Ebola
in
remote
Liberia
– Paired
control
study:
Half
from
epilepsy
pa2ents
and
half
from
healthy
volunteers
– Geographic
and
social
group
sub-­‐analysis
shows
all
affected
~equally

6. Twi.er
Tracking
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
6
Most
common
images:
Risk
map,
lab
work
(britain),
joke
cartoon,
EBV
rally

7. Liberia
Forecasts
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
7

8. Liberia
Forecasts
rI:
0.95
rH:
0.65
rF:
0.61
R0
total:
2.22
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
8
8/6
–
8/12
8/13
–
8/19
8/20
–
8/26
8/27
–
9/02
9/3
–
9/9
9/10
–
9/16
Actual
163
232
296
296
-­‐-­‐
-­‐-­‐
Forecast
133
176
234
310
410
543
Model
Parameters
'alpha':1/12,
'beta_I':0.17950,
'beta_H':0.062036,
'beta_F':0.489256,
'gamma_h':0.308899,
'gamma_d':0.075121,
'gamma_I':0.050000,
'gamma_f':0.496443,
'delta_1':.5,
'delta_2':.5,
'dx':0.510845

9. Liberia
Vaccina2ons
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
9
20%
of
popula2on
Vaccinated
on
Nov
1st
and
Jan
1st
Addi2onal
Infec2ons
Prevented
(by
April
2015):
Nov
1st
-­‐
~275k
Jan
1st
-­‐
~225k

10. New
model
for
Liberia
• Due
to
con2nued
underes2ma2on,
have
refit
model
– Small
increases
in
betas
change
the
fit
compared
to
“stable”
fit
of
last
3
weeks
– May
shid
to
this
model
for
future
forecasts
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
10

11. Sierra
Leone
Epi
Details
• asdfsdf
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
11
By
Sierra
Leone
MoH
has
1077
cases
(vs.
1026
as
reported
by
WHO)

12. Sierra
Leone
Forecasts
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
12

13. Sierra
Leone
Forecasts
rI:0.85
rH:0.74
rF:0.31
R0
total:
1.90
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
13
8/6
–
8/12
8/13
–
8/19
8/20
–
8/26
8/27
–
9/02
9/3
–
9/9
9/10
–
9/16
Actual
143
93
100
-­‐-­‐
-­‐-­‐
-­‐-­‐
Forecast
135
168
209
260
324
405
Model
Parameters
'alpha':1/10
'beta_I':0.164121
'beta_H':0.048990
'beta_F':.16
'gamma_h':0.296
'gamma_d':0.044827
'gamma_I':0.055
'gamma_f':0.25
'delta_1':.55
delta_2':.55
'dx':0.58

14. Sierra
Leone
Vaccina2ons
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
14
100k
on
Nov
1st
200k
on
Jan
1st
Addi2onal
Infec2ons
prevented
(by
April
2015)
Nov
1st
-­‐
~6k
Jan
1st
-­‐
~7.5k

15. All
Countries
Forecasts
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
15
rI:0.85
rH:0.74
rF:0.31
Overal:1.90

16. All
Countries
Vaccina2ons
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
16
100k
on
Nov
1st
200k
on
Jan
1st
Addi2onal
Infec2ons
prevented
(by
April
2015)
Nov
1st
-­‐
~3.2k
Jan
1st
-­‐
~4.0k
• Need
more
than
just
vaccine
to
interupt
transmission

17. Extrac2ng
the
Guinea
experience
• Result:
Not
enough
informa2on
in
early
slight
decrease
to
harvest
meaningful
impacts.
– Model
won’t
fit
well
• Conclusion:
Likely
need
to
wait
another
week
or
so
to
assess
impacts
of
recent
new
push
on
interven2ons
to
incorporate
their
impact
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
17

18. Long-­‐term
Opera2onal
Es2mates
• Based
on
forced
bend
through
extreme
reduc2on
in
transmission
coefficients,
no
evidence
to
support
bends
at
these
points
– Long
DRAFT
term
–
projecNot
2ons
are
for
unstable
a.ribu2on
or
distribu2on
18
Turn
from
8-­‐26
End
from
8-­‐26
Total
Case
EsHmate
1
month
6
months
15,800
1
month
18
months
31,300
3
months
6
months
64,300
3
months
18
months
120,000
6
months
9
months
599,000
6
months
18
months
857,000

19. Next
Steps
• Detailed
HCW
infec2on
analysis
underway
– Looking
at
exposure
and
infec2ons
in
Liberia
to
assess
the
a.ri2on
rates
of
HCW
under
current
condi2ons
• Ini2al
version
of
Sierra
Leone
constructed
– Ini2al
look
at
subloca2on
modeling
required
a
re-­‐
adjustment
– Should
start
simula2ons
this
week
• Build
similar
versions
for
other
affected
countries
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
19

20. Next
steps
• Publica2ons
– One
submi.ed,
another
in
the
works
– 2
quick
communica2ons
in
prep
• Problems
appropriate
for
agent-­‐based
approach
– Logis2cal
ques2ons
surrounding
delivery
and
use
of
medical
supplies
– Effects
of
limited
HCW
both
direct
and
indirect
– Synthe2c
outbreaks
to
compare
to
what
we’ve
observed
of
this
one,
to
es2mate
true
size
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
20

21. Suppor2ng
material
describing
model
structure,
and
previous
results
APPENDIX
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
21

22. Legrand
et
al.
Model
Descrip2on
Susceptible
Exposed
not infectious
Infectious
Symptomatic
Hospitalized
Infectious
Funeral
Infectious
Removed
Recovered and immune
or dead and buried
Legrand,
J,
R
F
Grais,
P
Y
Boelle,
A
J
Valleron,
and
A
Flahault.
“Understanding
the
Dynamics
of
Ebola
Epidemics”
Epidemiology
and
Infec1on
135
(4).
2007.
Cambridge
University
Press:
610–21.
doi:10.1017/S0950268806007217.
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
22

23. Compartmental
Model
• Extension
of
model
proposed
by
Legrand
et
al.
Legrand,
J,
R
F
Grais,
P
Y
Boelle,
A
J
Valleron,
and
A
Flahault.
“Understanding
the
Dynamics
of
Ebola
Epidemics”
Epidemiology
and
Infec1on
135
(4).
2007.
Cambridge
University
Press:
610–21.
doi:10.1017/S0950268806007217.
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
23

24. Legrand
et
al.
Approach
• Behavioral
changes
to
reduce
transmissibili2es
at
specified
days
• Stochas2c
implementa2on
fit
to
two
historical
outbreaks
– Kikwit,
DRC,
1995
– Gulu,
Uganda,
2000
• Finds
two
different
“types”
of
outbreaks
– Community
vs.
Funeral
driven
outbreaks
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
24

25. Parameters
of
two
historical
outbreaks
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
25

26. NDSSL
Extensions
to
Legrand
Model
• Mul2ple
stages
of
behavioral
change
possible
during
this
prolonged
outbreak
• Op2miza2on
of
fit
through
automated
method
• Experiment:
– Explore
“degree”
of
fit
using
the
two
different
outbreak
types
for
each
country
in
current
outbreak
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
26

27. Op2mized
Fit
Process
• Parameters
to
explored
selected
– Diag_rate,
beta_I,
beta_H,
beta_F,
gamma_I,
gamma_D,
gamma_F,
gamma_H
– Ini2al
values
based
on
two
historical
outbreak
• Op2miza2on
rou2ne
– Runs
model
with
various
permuta2ons
of
parameters
– Output
compared
to
observed
case
count
– Algorithm
chooses
combina2ons
that
minimize
the
difference
between
observed
case
counts
and
model
outputs,
selects
“best”
one
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
27

28. Fi.ed
Model
Caveats
• Assump2ons:
– Behavioral
changes
effect
each
transmission
route
similarly
– Mixing
occurs
differently
for
each
of
the
three
compartments
but
uniformly
within
• These
models
are
likely
“overfi.ed”
– Many
combos
of
parameters
will
fit
the
same
curve
– Guided
by
knowledge
of
the
outbreak
and
addi2onal
data
sources
to
keep
parameters
plausible
– Structure
of
the
model
is
supported
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
28

29. No2onal
US
es2mates
Approach
• Get
disease
parameters
from
fi.ed
model
in
West
Africa
• Put
into
CNIMS
plauorm
– ISIS
simula2on
GUI
– Modify
to
represent
US
• Example
Experiment:
– 100
replicates
– One
case
introduc2on
into
Washington
DC
– Simulate
for
3
weeks
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
29

30. No2onal
US
es2mates
Assump2ons
• Under
assump2on
that
Ebola
case,
arrives
and
doesn’t
seek
care
and
avoids
detec2on
throughout
illness
• CNIMS
based
simula2ons
– Agent-­‐based
models
of
popula2ons
with
realis2c
social
networks,
built
up
from
high
resolu2on
census,
ac2vity,
and
loca2on
data
• Assume:
– Transmission
calibrated
to
R0
of
3.5
if
transmission
is
like
flu
– Reduced
transmission
Ebola
70%
less
likely
to
infect
in
home
and
95%
less
likely
to
infect
outside
of
home
than
respiratory
illness
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
30

31. No2onal
US
es2mates
Example
An Epi Plot
Cell=7187
Replicate Mean
Overall Mean
0 5 10 15 20
0 1 2 3 4 5 6
Cumulative Infections
100
replicates
Day
Mean
of
1.8
cases
Max
of
6
cases
Majority
only
one
ini2al
case
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
31