Modeling the Ebola Outbreak in West Africa, October 7th 2014 update

DRAFT – Not for a.ribu2on or distribu2on

Modeling the Ebola Outbreak in West Africa, 2014

Oct 7th Update

Bryan Lewis PhD, MPH ([email protected]) Caitlin Rivers MPH, Eric Lofgren PhD, James Schli., Ka2e 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 Barre. PhD

Technical Report #14-‐110


Currently Used Data

●  Data from WHO, MoH Liberia, and MoH Sierra Leone, available at h.ps://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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Cases Deaths Guinea 1157 710 Liberia 3688 1998 Nigeria 22 8 Sierra Leone 2407 622 Total 7274 3338


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 Poli2co

•  Suspect cases con2nue to be iden2fied in the US, currently a pa2ent in Dallas (previous nega2ves from CA, NY, NM, FL) WaPo

•  Sierra Leone’s repor2ng s2ll inconsistent Crawford Killian

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Liberia – Case Loca2ons

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Liberia – Contact Tracing

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Liberia Forecasts

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8/25 – 8/31

9/01– 9/07

9/08 – 9/14

9/15 – 9/21

9/22 – 9/28

9/29-‐10/5

10/06-‐10/12

Actual 386 355 639 560 416 -‐-‐ -‐-‐

Forecast 395 525 698 927 1232 1636 2173

Forecast performance

Reproduc2ve Number Community 1.3 Hospital 0.4 Funeral 0.5 Overall 2.2

52% of Infected are hospitalized


Prevalence of Cases

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Week People in H+I

9/28/2014 1228

10/05/2014 1631

10/12/2014 2167

10/19/2014 2878

10/26/2014 3821

11/02/2014 5071

11/16/2014 8911


Sierra Leone Forecasts

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Forecast performance

41% of cases are hospitalized

8/25 – 8/31

9/01– 9/07

9/08 – 9/14

9/15 – 9/21

9/22-‐ 9/28

9/29 – 10/05

10/06-‐10/12

Actual 196 219 194 274 332 -‐-‐ -‐-‐

Forecast 267 333 413 512 635 786 974


Prevalence in SL

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Week People in H+I

9/28/2014 668

10/05/2014 828

10/12/2014 1026

10/19/2014 1271

10/26/2014 1573

11/02/2014 1947

11/16/2014 2978


All Countries Forecasts

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rI: 1.1 rH:0.4 rF:0.3 Overall:1.7


Combined Forecasts

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8/18 – 8/24

8/25 – 8/31

9/1– 9/7 9/8 – 9/14

9/15-‐ 9/21

9/22 – 9/28

9/29 – 10/5

10/6 -‐10/12

Actual 559 783 681 959 917 915 -‐-‐ -‐-‐

Forecast 483 578 693 830 994 1191 1426 1426


Experiments

•  Hospital bed es2mate calcula2ons •  Reduc2on in 2me to hospitaliza2on •  Improvements in 2me from symptom onset to hospitaliza2on

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Hospital Beds – Prelim analysis

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Cases on Feb 1 Oct 1 245k Nov 1 312k Dec 1 391k Jan 1 475k No beds 533k

Impact in Liberia, beds only

16% hospitaliza2on ra2o -‐> 70% Beta_H reduc2on by 90%


Hospital Beds – Prelim analysis

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Cases on Feb 1 Oct 1 73k Nov 1 135k Dec 1 230k Jan 1 375k No beds 533k

Impact in Liberia, beds and proper burial

16% hospitaliza2on ra2o -‐> 70% Beta_H reduc2on by 90% Beta_F reduc2on by 90%


Hospital beds – Prelim analysis

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Impact in Liberia, beds + proper burial + shortened 2me to hospitaliza2on


Hospital beds – Prelim analysis

5 days 3 days 1 days

Oct 1 52k 25k 10k

Nov 1 108k 65k 31k

Dec 1 206k 152k 92k

Jan 1 358k 318k 2506

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Cumula2ve cases in Liberia on Feb 1 with reduced beta_H, reduced beta_F, and shortened 2me to hospitaliza2on


Case hospitaliza2on ra2o & 2me to hospitaliza2on

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Replica2on BARDA results

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Op2mal center placement

Preliminary op2miza2on using road networks and popula2on centers

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Agent-‐based Simula2ons Progress

•  Regional travel method, developed –  Implementa2on working this week

•  Interven2onal support designed for –  Increasing hospitaliza2on level – Be.er burial – Decreasing 2me to hospitaliza2on

•  Capacity monitoring at ETU/ECU designed – Need some bounds on experimental design

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APPENDIX Suppor2ng material describing model structure, and addi2onal results

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Legrand et al. Model Descrip2on

Exposednot infectious

InfectiousSymptomatic

RemovedRecovered and immune

or dead and buried

Susceptible

HospitalizedInfectious

FuneralInfectious

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.

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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 Infec1on 135 (4). 2007. Cambridge University Press: 610–21. doi:10.1017/S0950268806007217.

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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

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Parameters of two historical outbreaks

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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

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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

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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

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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 Opera2onal Es2mates

•  Based on forced bend through extreme reduc2on in transmission coefficients, no evidence to support bends at these points –  Long term projec2ons are unstable

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Turn from 8-‐26

End from 8-‐26

Total Case EsXmate

1 month 3 months 13,400



3 months 6 months 64,300





Science

Modeling the Ebola Outbreak in West Africa, October 7th 2014 update