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 Schlitt, Katie Dunphy,

Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barrett PhD

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Currently Used WHO DataCases 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.

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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

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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

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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

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Twitter TrackingMost common images:

Risk map, lab work (britain), joke cartoon, EBV rally

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Liberia Forecasts

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Liberia Forecasts

rI: 0.95rH: 0.65rF: 0.61R0 total: 2.22

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

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Liberia Vaccinations

20% of populationVaccinated onNov 1st and Jan 1st

AdditionalInfections Prevented(by April 2015):Nov 1st - ~275k Jan 1st - ~225k

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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

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Sierra Leone Epi Details

• asdfsdfBy Sierra Leone MoH has 1077 cases (vs. 1026 as reported by WHO)

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Sierra Leone Forecasts

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Sierra Leone Forecasts

rI:0.85rH:0.74rF:0.31R0 total: 1.90

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':.55delta_2':.55'dx':0.58

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Sierra Leone Vaccinations

100k on Nov 1st

200k on Jan 1st

AdditionalInfections prevented(by April 2015)Nov 1st - ~6kJan 1st - ~7.5k

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All Countries Forecasts

rI:0.85rH:0.74rF:0.31Overal:1.90

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All Countries Vaccinations

100k on Nov 1st

200k on Jan 1st

AdditionalInfections prevented(by April 2015)Nov 1st - ~3.2kJan 1st - ~4.0k

• Need more than just vaccine to interupt transmission

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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

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Long-term Operational Estimates

• Based on forced bend through extreme reduction in transmission coefficients, no evidence to support bends at these points– Long term projections are unstable

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 64,300

3 months 18 months 120,000

6 months 9 months 599,000

6 months 18 months 857,000

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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 re-

adjustment– Should start simulations this week

• Build similar versions for other affected countries

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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

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APPENDIXSupporting material describing model structure, and previous results

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Legrand et al. Model Description

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.

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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.

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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

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Parameters of two historical outbreaks

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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

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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

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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

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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

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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

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Notional US estimates Example

100 replicatesMean of 1.8 casesMax of 6 casesMajority only one initial case