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Modeling 
the 
Ebola 
Outbreak 
in 
West 
Africa, 
2014 
August 
4th 
Update 
Bryan 
Lewis 
PhD, 
MPH 
(blewis@vbi.vt.edu) 
Caitlin 
Rivers 
MPH, 
Stephen 
Eubank 
PhD, 
Madhav 
Marathe 
PhD, 
and 
Chris 
Barre. 
PhD 
Technical 
Report 
#14-­‐096 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Overview 
• Epidemiological 
Update 
– Country 
by 
country 
analysis 
– Details 
from 
Liberian 
outbreak 
• Compartmental 
Model 
– Descrip2on 
– Comparisons 
with 
different 
disease 
parameters 
– Long 
term 
projec2ons 
• Preliminary 
back 
of 
envelope 
look 
at 
US 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Epidemiological 
Update 
• Confirmed 
case 
imported 
into 
Lagos 
via 
air 
travel 
– 
59 
contacts 
being 
monitored, 
but 
many 
others 
are 
lost 
to 
followup 
• Outbreak 
in 
Guinea 
has 
picked 
up 
again 
• Two 
new 
areas 
of 
Liberia 
are 
repor2ng 
cases 
• 83 
new 
cases 
in 
Sierra 
Leone 
from 
July 
20-­‐27 
• 2 
American 
Health 
workers 
now 
cases 
– One 
in 
Atlanta 
being 
treated 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Epidemiological 
Update 
Cases 
Deaths 
Guinea 
460 
339 
Liberia 
329 
156 
Sierra 
Leone 
533 
233 
Total 
1322 
728 
● Data 
reported 
by 
WHO 
on 
July 
31 
for 
cases 
as 
of 
July 
27 
● 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
Liberia 
● Major 
ports 
of 
entry 
are 
now 
closed 
● Two 
previously 
unaffected 
coun2es 
are 
inves2ga2ng 
suspected 
cases 
● Con2nued 
transmission 
via 
funerary 
prac2ces 
s2ll 
suspected 
● Transmission 
among 
healthcare 
workers 
con2nued 
● Significant 
resistance 
in 
the 
community, 
par2cularly 
Lofa 
county. 
Pa2ents 
are 
concealed, 
refuse 
follow 
up, 
buried 
secretly. 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Detailed 
data 
from 
Liberia 
• Healthcare 
work 
infec2ons 
and 
contact 
tracing 
info 
captured 
• More 
details 
than 
aggregates 
could 
help 
with 
es2mates 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Sierra 
Leone 
• 83 
new 
cases 
were 
reported 
between 
July 
20 
and 
27, 
a 
massive 
increase. 
• Sierra 
Leone 
now 
has 
the 
most 
cases 
of 
the 
three 
affected 
countries 
• A 
confirmed 
pa2ent 
lec 
isola2on 
in 
the 
capital 
city, 
reportedly 
due 
to 
“fear 
and 
mistrust 
of 
health 
workers” 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Guinea 
• Worrying 
spike 
in 
cases 
from 
July 
20-­‐27 
repor2ng 
period, 
acer 
a 
prolonged 
lull. 
• WHO 
asserts 
this 
suggests 
“undetected 
chains 
of 
transmission 
existed 
in 
the 
community” 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Susceptible 
Exposed 
not infectious 
Infectious 
Symptomatic 
Hospitalized 
Infectious 
MODEL 
DESCRIPTION 
Funeral 
Infectious 
Removed 
Recovered and immune 
or dead and buried 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Compartmental 
Approach 
• 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
Legrand 
et 
al. 
Model 
Descrip2on 
Susceptible 
Exposed 
not infectious 
Infectious 
Symptomatic 
Hospitalized 
Infectious 
Funeral 
Infectious 
Removed 
Recovered and immune 
or dead and buried 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
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
Parameters 
of 
two 
historical 
outbreaks 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
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
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
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” 
– Guided 
by 
knowledge 
of 
the 
outbreak 
to 
keep 
parameters 
plausible 
– Structure 
of 
the 
model 
is 
published 
and 
defensible 
– Many 
combos 
of 
parameters 
will 
fit 
the 
same 
curve 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Guinea 
Fi.ed 
Model 
In 
progress 
This 
outbreak 
is 
difficult 
to 
fit, 
with 
so 
many 
seeming 
behavioral 
shics 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Liberia 
Fi.ed 
Models 
Assuming 
no 
impact 
from 
ongoing 
response 
and 
DRC 
parameter 
fit 
is 
correct: 
83 
cases 
in 
next 
14 
days 
Assuming 
no 
impact 
from 
ongoing 
response 
and 
Uganda 
parameter 
fit 
is 
correct: 
63 
cases 
in 
next 
14 
days 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Liberia 
Fi.ed 
Models 
Model 
Parameters 
Liberia 
Disease 
Parameters 
for 
Model 
FiAng 
UgandaOut 
Uganda_in 
DRCOut 
DRC_in 
beta_F 
0.852496 
1.093286 
0.020287 
0.066 
beta_H 
0.107974 
0.113429 
0.00057 
0.001714 
beta_I 
0.083646 
0.084 
0.465238 
0.504571 
dx 
0.2 
0.65 
0.9 
0.67 
gamma_I 
0.533852 
0.474164 
0.626551 
0.474164 
gamma_d 
0.138182 
0.125 
0.120216 
0.104167 
gamma_f 
0.720525 
0.5 
0.719349 
0.5 
gamma_h 
0.203924 
0.238095 
0.330794 
0.2 
Score 
14742 
NA 
9694 
NA 
No 
behavioral 
Changes 
included 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Liberia 
Fi.ed 
Models 
Sources 
of 
InfecFons 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Sierra 
Leone 
Fi.ed 
Model 
Assuming 
no 
impact 
from 
ongoing 
response 
and 
DRC 
parameter 
fit 
is 
correct: 
101 
cases 
in 
next 
14 
days 
Assuming 
no 
impact 
from 
ongoing 
response 
and 
Uganda 
parameter 
fit 
is 
correct: 
107 
cases 
in 
next 
14 
days 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Sierra 
Leone 
Fi.ed 
Model 
Model 
Parameters 
Sierra 
Leone 
Disease 
Parameters 
for 
Model 
FiAng 
UgandaOut 
Uganda_in 
DRCOut 
DRC_in 
beta_F 
1.253475 
1.093286 
0.058504 
0.066 
beta_H 
0.067821 
0.113429 
0.000000 
0.001714 
beta_I 
0.090063 
0.084 
0.308796 
0.504571 
dx 
0.669891 
0.65 
0.604919 
0.67 
gamma_I 
0.460938 
0.437148 
0.687977 
0.437148 
gamma_d 
0.159662 
0.125 
0.090552 
0.104167 
gamma_f 
0.550443 
0.5 
0.596496 
0.5 
gamma_h 
0.159662 
0.238095 
0.226723 
0.2 
Score 
78487 
NA 
76891 
NA 
No 
behavioral 
Changes 
included 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Sierra 
Leone 
Fi.ed 
Models 
Sources 
of 
InfecFons 
DRC 
R0 
es2mates 
rI: 
1.40278302774 
rH: 
2.85436942329e-­‐10 
rF: 
0.151864780392 
Overall: 
1.55464780842 
Uganda 
R0 
es2mates 
rI: 
0.424081180257 
rH: 
6.70236711791e-­‐10 
rF: 
1.4124823235 
Overall: 
1.83656350443 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Model 
Fiong 
Conclusions 
• Given: 
– Many 
sets 
of 
parameters 
can 
yield 
“reasonable” 
fits 
with 
totally 
different 
transmission 
drivers 
– These 
different 
parameter 
sets 
offer 
similar 
es2mated 
projec2ons 
of 
future 
cases 
– Coarseness 
of 
the 
case 
counts 
and 
inability 
to 
es2mate 
under/ 
over 
case 
ascertainment 
• Can 
not 
account 
for 
all 
uncertainty, 
thus 
es2mates 
are 
very 
uncertain: 
– Model 
structure 
and 
parameters 
allow 
for 
over-­‐fiong 
– Not 
enough 
informa2on 
in 
current 
case 
count 
curve 
to 
limit 
this 
uncertainty 
– Need 
more 
informa2on 
to 
characterize 
the 
type 
of 
outbreak 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
No2onal 
Long-­‐term 
Projec2ons 
• Start 
with 
“best” 
fit 
model 
(example 
purposes: 
Sierra 
Leone 
model 
fit 
from 
Uganda 
params) 
• Induce 
behavioral 
change 
in 
2 
weeks 
that 
bends 
epidemic 
over 
such 
that 
it 
ends 
at 
6m, 
12m, 
18m 
• Es2mate 
impact 
– Example: 
Sierra 
Leone 
@ 
6m 
– Cases: 
~900 
more 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
No2onal 
US 
es2mates 
• 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: 
– Reduced 
transmission 
routes 
in 
US 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
No2onal 
US 
es2mates 
Approach 
• Get 
disease 
parameters 
from 
fi.ed 
model 
in 
West 
Africa 
• Put 
into 
CNIMS 
plarorm 
– 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
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
Next 
steps 
• Seek 
data 
to 
choose 
most 
appropriate 
model 
– Detailed 
Liberia 
MoH 
with 
HCW 
data 
– Parse 
news 
reports 
to 
es2mate 
main 
drivers 
of 
transmission 
• Patch 
modeling 
with 
flows 
between 
regions 
from 
road 
networks 
• Refine 
es2mates 
for 
US 
– Find 
more 
informa2on 
on 
characteris2cs 
of 
disease, 
explore 
parameter 
ranges 
• Gather 
more 
data 
for 
West 
African 
region 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Patch 
Modeling 
Combines 
compartmental 
models 
with 
Niche 
modeling 
to 
explore 
larger 
scale 
dynamics. 
Can 
help 
understand 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Popula2on 
Construc2on 
-­‐ 
Global 
New Focus 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Popula2on 
Construc2on 
-­‐ 
Pipeline 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on
Pop 
Construc2on 
– 
New 
Countries 
Liberia: 
§ Limited 
demographic 
informa2on 
§ h.p://www.tlcafrica.com/lisgis/lisgis.htm 
§ h.p://www.na2onmaster.com/country-­‐info/profiles/Liberia/People 
§ OpenStreetMap 
data 
(OSM 
data 
file 
is 
4.1 
MB, 
so 
limited) 
§ h.p://download.geofabrik.de/africa.html 
Sierra 
Leone: 
§ Some 
demographic 
informa2on 
§ h.p://www.sta2s2cs.sl 
§ h.p://www.na2onmaster.com/country-­‐info/profiles/Sierra-­‐Leone/People 
§ h.p://www.sta2s2cs.sl/reports_to_publish_2010/popula2on_profile_of_sierra_leone_2010.pdf 
§ OpenStreetMap 
data 
(OSM 
data 
file 
is 
10.2 
MB) 
Guinea: 
§ Some 
demographic 
informa2on 
§ h.p://www.stat-­‐guinee.org 
(french) 
§ h.p://www.na2onmaster.com/country-­‐info/profiles/Guinea/People 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on 
§ OpenStreetMap 
data 
(OSM 
data 
file 
is 
15.6 
MB)
Conclusions 
• S2ll 
need 
more 
data 
– Working 
on 
gathering 
West 
Africa 
popula2on 
and 
movement 
data 
– News 
reports 
and 
official 
data 
sources 
need 
to 
be 
analyzed 
to 
be.er 
understand 
the 
major 
drivers 
of 
infec2on 
• From 
available 
data 
and 
in 
the 
absence 
of 
significant 
mi2ga2on 
outbreak 
in 
Africa 
looks 
to 
con2nue 
to 
produce 
significant 
numbers 
of 
cases 
• Expert 
opinion 
and 
preliminary 
simula2ons 
support 
limited 
spread 
in 
US 
context 
DRAFT 
– 
Not 
for 
a.ribu2on 
or 
distribu2on

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Modeling the Ebola Outbreak in West Africa, August 4th 2014 update

  • 1. Modeling the Ebola Outbreak in West Africa, 2014 August 4th Update Bryan Lewis PhD, MPH (blewis@vbi.vt.edu) Caitlin Rivers MPH, Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barre. PhD Technical Report #14-­‐096 DRAFT – Not for a.ribu2on or distribu2on
  • 2. Overview • Epidemiological Update – Country by country analysis – Details from Liberian outbreak • Compartmental Model – Descrip2on – Comparisons with different disease parameters – Long term projec2ons • Preliminary back of envelope look at US DRAFT – Not for a.ribu2on or distribu2on
  • 3. Epidemiological Update • Confirmed case imported into Lagos via air travel – 59 contacts being monitored, but many others are lost to followup • Outbreak in Guinea has picked up again • Two new areas of Liberia are repor2ng cases • 83 new cases in Sierra Leone from July 20-­‐27 • 2 American Health workers now cases – One in Atlanta being treated DRAFT – Not for a.ribu2on or distribu2on
  • 4. Epidemiological Update Cases Deaths Guinea 460 339 Liberia 329 156 Sierra Leone 533 233 Total 1322 728 ● Data reported by WHO on July 31 for cases as of July 27 ● 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
  • 5. Liberia ● Major ports of entry are now closed ● Two previously unaffected coun2es are inves2ga2ng suspected cases ● Con2nued transmission via funerary prac2ces s2ll suspected ● Transmission among healthcare workers con2nued ● Significant resistance in the community, par2cularly Lofa county. Pa2ents are concealed, refuse follow up, buried secretly. DRAFT – Not for a.ribu2on or distribu2on
  • 6. Detailed data from Liberia • Healthcare work infec2ons and contact tracing info captured • More details than aggregates could help with es2mates DRAFT – Not for a.ribu2on or distribu2on
  • 7. Sierra Leone • 83 new cases were reported between July 20 and 27, a massive increase. • Sierra Leone now has the most cases of the three affected countries • A confirmed pa2ent lec isola2on in the capital city, reportedly due to “fear and mistrust of health workers” DRAFT – Not for a.ribu2on or distribu2on
  • 8. Guinea • Worrying spike in cases from July 20-­‐27 repor2ng period, acer a prolonged lull. • WHO asserts this suggests “undetected chains of transmission existed in the community” DRAFT – Not for a.ribu2on or distribu2on
  • 9. Susceptible Exposed not infectious Infectious Symptomatic Hospitalized Infectious MODEL DESCRIPTION Funeral Infectious Removed Recovered and immune or dead and buried DRAFT – Not for a.ribu2on or distribu2on
  • 10. Compartmental Approach • 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
  • 11. Legrand et al. Model Descrip2on Susceptible Exposed not infectious Infectious Symptomatic Hospitalized Infectious Funeral Infectious Removed Recovered and immune or dead and buried DRAFT – Not for a.ribu2on or distribu2on
  • 12. 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
  • 13. Parameters of two historical outbreaks DRAFT – Not for a.ribu2on or distribu2on
  • 14. 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
  • 15. 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
  • 16. 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” – Guided by knowledge of the outbreak to keep parameters plausible – Structure of the model is published and defensible – Many combos of parameters will fit the same curve DRAFT – Not for a.ribu2on or distribu2on
  • 17. Guinea Fi.ed Model In progress This outbreak is difficult to fit, with so many seeming behavioral shics DRAFT – Not for a.ribu2on or distribu2on
  • 18. Liberia Fi.ed Models Assuming no impact from ongoing response and DRC parameter fit is correct: 83 cases in next 14 days Assuming no impact from ongoing response and Uganda parameter fit is correct: 63 cases in next 14 days DRAFT – Not for a.ribu2on or distribu2on
  • 19. Liberia Fi.ed Models Model Parameters Liberia Disease Parameters for Model FiAng UgandaOut Uganda_in DRCOut DRC_in beta_F 0.852496 1.093286 0.020287 0.066 beta_H 0.107974 0.113429 0.00057 0.001714 beta_I 0.083646 0.084 0.465238 0.504571 dx 0.2 0.65 0.9 0.67 gamma_I 0.533852 0.474164 0.626551 0.474164 gamma_d 0.138182 0.125 0.120216 0.104167 gamma_f 0.720525 0.5 0.719349 0.5 gamma_h 0.203924 0.238095 0.330794 0.2 Score 14742 NA 9694 NA No behavioral Changes included DRAFT – Not for a.ribu2on or distribu2on
  • 20. Liberia Fi.ed Models Sources of InfecFons DRAFT – Not for a.ribu2on or distribu2on
  • 21. Sierra Leone Fi.ed Model Assuming no impact from ongoing response and DRC parameter fit is correct: 101 cases in next 14 days Assuming no impact from ongoing response and Uganda parameter fit is correct: 107 cases in next 14 days DRAFT – Not for a.ribu2on or distribu2on
  • 22. Sierra Leone Fi.ed Model Model Parameters Sierra Leone Disease Parameters for Model FiAng UgandaOut Uganda_in DRCOut DRC_in beta_F 1.253475 1.093286 0.058504 0.066 beta_H 0.067821 0.113429 0.000000 0.001714 beta_I 0.090063 0.084 0.308796 0.504571 dx 0.669891 0.65 0.604919 0.67 gamma_I 0.460938 0.437148 0.687977 0.437148 gamma_d 0.159662 0.125 0.090552 0.104167 gamma_f 0.550443 0.5 0.596496 0.5 gamma_h 0.159662 0.238095 0.226723 0.2 Score 78487 NA 76891 NA No behavioral Changes included DRAFT – Not for a.ribu2on or distribu2on
  • 23. Sierra Leone Fi.ed Models Sources of InfecFons DRC R0 es2mates rI: 1.40278302774 rH: 2.85436942329e-­‐10 rF: 0.151864780392 Overall: 1.55464780842 Uganda R0 es2mates rI: 0.424081180257 rH: 6.70236711791e-­‐10 rF: 1.4124823235 Overall: 1.83656350443 DRAFT – Not for a.ribu2on or distribu2on
  • 24. Model Fiong Conclusions • Given: – Many sets of parameters can yield “reasonable” fits with totally different transmission drivers – These different parameter sets offer similar es2mated projec2ons of future cases – Coarseness of the case counts and inability to es2mate under/ over case ascertainment • Can not account for all uncertainty, thus es2mates are very uncertain: – Model structure and parameters allow for over-­‐fiong – Not enough informa2on in current case count curve to limit this uncertainty – Need more informa2on to characterize the type of outbreak DRAFT – Not for a.ribu2on or distribu2on
  • 25. No2onal Long-­‐term Projec2ons • Start with “best” fit model (example purposes: Sierra Leone model fit from Uganda params) • Induce behavioral change in 2 weeks that bends epidemic over such that it ends at 6m, 12m, 18m • Es2mate impact – Example: Sierra Leone @ 6m – Cases: ~900 more DRAFT – Not for a.ribu2on or distribu2on
  • 26. No2onal US es2mates • 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: – Reduced transmission routes in US DRAFT – Not for a.ribu2on or distribu2on
  • 27. No2onal US es2mates Approach • Get disease parameters from fi.ed model in West Africa • Put into CNIMS plarorm – 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
  • 28. 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
  • 29. Next steps • Seek data to choose most appropriate model – Detailed Liberia MoH with HCW data – Parse news reports to es2mate main drivers of transmission • Patch modeling with flows between regions from road networks • Refine es2mates for US – Find more informa2on on characteris2cs of disease, explore parameter ranges • Gather more data for West African region DRAFT – Not for a.ribu2on or distribu2on
  • 30. Patch Modeling Combines compartmental models with Niche modeling to explore larger scale dynamics. Can help understand DRAFT – Not for a.ribu2on or distribu2on
  • 31. Popula2on Construc2on -­‐ Global New Focus DRAFT – Not for a.ribu2on or distribu2on
  • 32. Popula2on Construc2on -­‐ Pipeline DRAFT – Not for a.ribu2on or distribu2on
  • 33. Pop Construc2on – New Countries Liberia: § Limited demographic informa2on § h.p://www.tlcafrica.com/lisgis/lisgis.htm § h.p://www.na2onmaster.com/country-­‐info/profiles/Liberia/People § OpenStreetMap data (OSM data file is 4.1 MB, so limited) § h.p://download.geofabrik.de/africa.html Sierra Leone: § Some demographic informa2on § h.p://www.sta2s2cs.sl § h.p://www.na2onmaster.com/country-­‐info/profiles/Sierra-­‐Leone/People § h.p://www.sta2s2cs.sl/reports_to_publish_2010/popula2on_profile_of_sierra_leone_2010.pdf § OpenStreetMap data (OSM data file is 10.2 MB) Guinea: § Some demographic informa2on § h.p://www.stat-­‐guinee.org (french) § h.p://www.na2onmaster.com/country-­‐info/profiles/Guinea/People DRAFT – Not for a.ribu2on or distribu2on § OpenStreetMap data (OSM data file is 15.6 MB)
  • 34. Conclusions • S2ll need more data – Working on gathering West Africa popula2on and movement data – News reports and official data sources need to be analyzed to be.er understand the major drivers of infec2on • From available data and in the absence of significant mi2ga2on outbreak in Africa looks to con2nue to produce significant numbers of cases • Expert opinion and preliminary simula2ons support limited spread in US context DRAFT – Not for a.ribu2on or distribu2on