Transcript September 2, 2014 - Network Dynamics & Simulation Science

Modeling the Ebola
Outbreak in West Africa, 2014
Sept 2nd Update
Bryan Lewis PhD, MPH ([email protected])
Caitlin Rivers MPH, Eric Lofgren PhD, James Schlitt, Katie Dunphy,
Stephen Eubank PhD, Madhav Marathe PhD,
and Chris Barrett PhD
Currently Used WHO Data
Cases
Deaths
Guinea
648
430
Liberia
1378
694
Sierra Leone
1026
422
17
6
3069
1563
Nigeria
Total
●
●
●
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.
2
Epi Notes
• Case identified in Senegal
– Guinean student, sought care in Dakar, identified
and quarantined 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 after recovering
from the virus, according to the World Health
Organization. CNN
3
Epi Notes
• Guinea riot in Nzerekore (2nd city) on Aug 29
– Market area “disinfected,” angry residents attack
HCW and hospital, “Ebola is a lie” BBC
• India quarantines 6 “high-risk” Ebola suspects
on Monday in New Delhi
– Among 181 passengers who arrived in India from
the affected western African countries HealthMap
4
Further evidence of endemic Ebola
• 1985 manuscript finds ~13% sero-prevalence of Ebola in remote Liberia
– Paired control study: Half from epilepsy patients and half from healthy volunteers
– Geographic and social group sub-analysis shows all affected ~equally
5
Twitter Tracking
Most common images:
Risk map, lab work (britain), joke cartoon, EBV rally
6
Liberia Forecasts
7
Liberia Forecasts
rI: 0.95
rH: 0.65
rF: 0.61
R0 total: 2.22
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
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
8
Liberia Vaccinations
20% of population
Vaccinated on
Nov 1st and Jan 1st
Additional
Infections Prevented
(by April 2015):
Nov 1st - ~275k
Jan 1st - ~225k
9
New model for Liberia
• Due to continued
underestimation,
have refit model
– Small increases in
betas change the
fit compared to
“stable” fit of last 3
weeks
– May shift to this
model for future
forecasts
10
Sierra Leone Epi Details
• asdfsdf
By Sierra Leone MoH has 1077 cases (vs. 1026 as reported by WHO)
11
Sierra Leone Forecasts
12
Sierra Leone Forecasts
rI:0.85
rH:0.74
rF:0.31
R0 total: 1.90
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
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
13
Sierra Leone Vaccinations
100k on Nov 1st
200k on Jan 1st
Additional
Infections prevented
(by April 2015)
Nov 1st - ~6k
Jan 1st - ~7.5k
14
All Countries Forecasts
rI:0.85
rH:0.74
rF:0.31
Overal:1.90
15
All Countries Vaccinations
100k on Nov 1st
200k on Jan 1st
Additional
Infections prevented
(by April 2015)
Nov 1st - ~3.2k
Jan 1st - ~4.0k
• Need more than just vaccine to interupt
transmission
16
Extracting the Guinea experience
• Result: Not enough
information 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
interventions to
incorporate their impact
17
Long-term Operational Estimates
Turn
from 8-26
End
from 8-26
Total Case
Estimate
1 month
6 months
15,800
1 month
18 months 31,300
3 months
6 months
3 months
18 months 120,000
6 months
9 months
6 months
18 months 857,000
64,300
599,000
• Based on forced bend through extreme reduction in transmission
coefficients, no evidence to support bends at these points
– Long term projections are unstable
18
Next Steps
• Detailed HCW infection analysis underway
– Looking at exposure and infections in Liberia to assess
the attrition rates of HCW under current conditions
• Initial version of Sierra Leone constructed
– Initial look at sublocation modeling required a readjustment
– Should start simulations this week
• Build similar versions for other affected countries
19
Next steps
• Publications
– One submitted, another in the works
– 2 quick communications in prep
• Problems appropriate for agent-based approach
– Logistical questions surrounding delivery and use of
medical supplies
– Effects of limited HCW both direct and indirect
– Synthetic outbreaks to compare to what we’ve
observed of this one, to estimate true size
20
Supporting material describing model structure, and previous results
APPENDIX
21
Legrand et al. Model Description
Susceptible
Exposed
not infectious
Infectious
Symptomatic
Hospitalized
Infectious
Funeral
Legrand, J, R F Grais, P Y Boelle, A J Valleron, and A
Flahault. “Understanding the Dynamics of Ebola
Epidemics” Epidemiology and Infection 135 (4). 2007.
Cambridge University Press: 610–21.
doi:10.1017/S0950268806007217.
Infectious
Removed
Recovered and immune
or dead and buried
22
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 Infection 135 (4). 2007. Cambridge
University Press: 610–21.
doi:10.1017/S0950268806007217.
23
Legrand et al. Approach
• Behavioral changes to reduce
transmissibilities at specified
days
• Stochastic implementation fit
to two historical outbreaks
– Kikwit, DRC, 1995
– Gulu, Uganda, 2000
• Finds two different “types” of
outbreaks
– Community vs. Funeral driven
outbreaks
24
Parameters of two historical outbreaks
25
NDSSL Extensions to Legrand Model
• Multiple stages of behavioral change possible
during this prolonged outbreak
• Optimization of fit through automated
method
• Experiment:
– Explore “degree” of fit using the two different
outbreak types for each country in current
outbreak
26
Optimized Fit Process
• Parameters to explored selected
– Diag_rate, beta_I, beta_H, beta_F, gamma_I, gamma_D,
gamma_F, gamma_H
– Initial values based on two historical outbreak
• Optimization routine
– Runs model with various
permutations of parameters
– Output compared to observed case
count
– Algorithm chooses combinations that
minimize the difference between
observed case counts and model
outputs, selects “best” one
27
Fitted Model Caveats
• Assumptions:
– Behavioral changes effect each transmission route
similarly
– Mixing occurs differently for each of the three
compartments but uniformly within
• These models are likely “overfitted”
– Many combos of parameters will fit the same curve
– Guided by knowledge of the outbreak and additional
data sources to keep parameters plausible
– Structure of the model is supported
28
Notional US estimates Approach
• Get disease parameters from fitted model in
West Africa
• Put into CNIMS platform
– ISIS simulation GUI
– Modify to represent US
• Example Experiment:
– 100 replicates
– One case introduction into Washington DC
– Simulate for 3 weeks
29
Notional US estimates Assumptions
• Under assumption that Ebola case, arrives and doesn’t
seek care and avoids detection throughout illness
• CNIMS based simulations
– Agent-based models of populations with realistic social
networks, built up from high resolution census, activity,
and location 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
30
Notional US estimates Example
An Epi Plot
Cell=7187
3
0
1
2
Cumulative Infections
4
5
6
Replicate Mean
Overall Mean
0
5
10
15
20
Day
100 replicates
Mean of 1.8 cases
Max of 6 cases
Majority only one initial case
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