Modeling the Ebola Outbreak in West Africa, 2014 Sept 30 th Update Bryan Lewis PhD, MPH ( [email protected]

) Caitlin Rivers MPH, Eric Lofgren PhD, James Schlitt, Katie Dunphy, Henning Mortveit PhD, Dawen Xie MS, Samarth Swarup PhD, Hannah Chungbaek, Keith Bisset PhD, Maleq Khan PhD, Chris Kuhlman PhD, Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barrett PhD

Guinea Liberia Nigeria Sierra Leone Total

Currently Used Data

Cases

1074 3362 22 2208

6666 Deaths

648 1830 8 605

3091

● ● ● ● Data from WHO, MoH Liberia, and MoH Sierra Leone, available at https://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.

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Epi Notes

• • • • Reports of efficacy of HIV drug “” lowering mortality CNN Two other physicians infected with Ebola back in US, one at NIH enrolled in vax trial Politico Suspect cases continue to be identified in the US, currently a patient in Dallas (previous negatives from CA, NY, NM, FL) WaPo Sierra Leone’s reporting still inconsistent Crawford Killian

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Liberia – Case Locations

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Liberia – Contact Tracing

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Liberia Forecasts

Forecast performance

8/18 8/24 8/25 – 8/31 9/01– 9/07 9/08 – 9/14 9/15 – 9/21 9/22 – 9/29 9/30 10/6

Actual 431 Forecast 314 368 417 421 555 620 738 558 981 - 1304 - 1733 52% of Infected are hospitalized Reproductive Number

Community

1.3

Hospital Funeral Overall

0.4

0.5

2.2

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Prevalence of Cases

Week

9/28/2014 10/05/2014 10/12/2014 10/19/2014 10/26/2014 11/02/2014 11/16/2014

People in H+I

1228 1631 2167 2878 3821 5071 8911

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Sierra Leone Forecasts

Forecast performance

8/25 – 8/31

Actual 196 Forecast 267

9/01– 9/07

219 333

9/08 – 9/14

194 413

9/15 – 9/21

274 512

9/22 9/28

332 635

9/29 – 10/06

- 786

10/06 10/12

- 974 41% of cases are hospitalized

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Prevalence in SL

Week People in H+I

9/28/2014 10/05/2014 10/12/2014 10/19/2014 10/26/2014 11/02/2014 11/16/2014 668 828 1026 1271 1573 1947 2978

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All Countries Forecasts

rI: 1.1

rH:0.4

rF:0.3

Overall:1.7

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Combined Forecasts

Actual Forecast

8/18 – 8/24

559 483

8/25 – 8/31

783 578

9/1– 9/7

681 693

9/8 – 9/14

959 830

9/15 9/21

917 994

9/22 – 9/28

- 1191

9/29 – 10/5

- 1426

10/6 10/12

- 1426

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Experiments

• • • Hospital bed estimate calculations Reduction in time to hospitalization Improvements in time from symptom onset to hospitalization

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Hospital Beds – Prelim analysis

Impact in Liberia, beds only Cases on Feb 1

Oct 1 245k Nov 1 Dec 1 Jan 1 312k 391k 475k No beds 533k 16% hospitalization ratio -> 70% Beta_H reduction by 90%

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Hospital Beds – Prelim analysis

Impact in Liberia, beds and proper burial Cases on Feb 1

Oct 1 Nov 1 73k 135k Dec 1 230k Jan 1 375k No beds 533k 16% hospitalization ratio -> 70% Beta_H reduction by 90% Beta_F reduction by 90%

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Hospital beds – Prelim analysis

Impact in Liberia, beds + proper burial + shortened time to hospitalization

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Hospital beds – Prelim analysis

Cumulative cases in Liberia on Feb 1 with reduced beta_H, reduced beta_F, and shortened time to hospitalization Oct 1 Nov 1 Dec 1 Jan 1

5 days

52k 108k 206k 358k

3 days

25k 65k 152k 318k

1 days

10k 31k 92k 2506

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Optimal center placement

Preliminary optimization using road networks and population centers

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Agent-based Simulations Progress

• • • Regional travel method, developed – Implementation working this week Interventional support designed for – Increasing hospitalization level – Better burial – Decreasing time to hospitalization Capacity monitoring at ETU/ECU designed – Need some bounds on experimental design

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Supporting material describing model structure, and additional results

APPENDIX

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Suscept ible

Legrand et al. Model Description

Exposed not infect ious Infect ious

Symptomatic

H ospit alized

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 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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Liberia model params

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Sierra Leone model params

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All Countries model params

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Long-term Operational Estimates

Turn from 8-26

1 month

End from 8-26

3 months

Total Case Estimate

13,400 1 month 1 month 6 months 18 months 15,800 31,300 3 months 3 months 3 months 6 months 6 months 6 months 12 months 18 months 12 months 18 months • Based on forced bend through extreme reduction in transmission coefficients, no evidence to support bends at these points – Long term projections are unstable 64,300 91,000 120,000 682,100 857,000

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