Modeling the Ebola Outbreak in West Africa, December 16th 2014 update

DRAFT – Not for a.ribu2on or distribu2on

Modeling the Ebola Outbreak in West Africa, 2014

December 16th Update

Bryan Lewis PhD, MPH ([email protected]) presen2ng on behalf of the Ebola Response Team of

Network Dynamics and Simula2on Science Lab from the Virginia Bioinforma2cs Ins2tute at Virginia Tech

Technical Report #14-‐130


NDSSL Ebola Response Team Staff: Abhijin Adiga, Kathy Alexander, Chris Barre., Richard Beckman, Keith Bisset, Jiangzhuo Chen, Youngyoun Chungbaek, Stephen Eubank, Sandeep Gupta, Maleq Khan, Chris Kuhlman, Eric Lofgren, Bryan Lewis, Achla Marathe, Madhav Marathe, Henning Mortveit, Eric Nordberg, Paula Stretz, Samarth Swarup, Meredith Wilson,Mandy Wilson, and Dawen Xie, with support from Ginger Stewart, Maureen Lawrence-‐Kuether, Kayla Tyler, Kathy Laskowski, Bill Marmagas Students: S.M. Arifuzzaman, Aditya Agashe, Vivek Akupatni, Caitlin Rivers, Pyrros Telionis, Jessie Gunter, Elisabeth Musser, James Schli., Youssef Jemia, Margaret Carolan, Bryan Kaperick, Warner Rose, Kara Harrison

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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 2,292 1,428 Liberia 7,797 3,177 Sierra Leone 8,273 1,768 Total 17,608 6,055


Liberia – Case Loca2ons

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Liberia infec2on rate

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Liberia Forecast

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11/03 to

11/09

11/10 to

11/16

11/17 to

11/23

11/24 to

11/30

12/1 to

12/7

12/8 to

12/14

12/15 to

12/21

12/22 to

12/28

12/29 to

1/04

1/05 to

1/11

1/12 to 1/8

Reported 362 185 187 156 431 111 -‐-‐

Newer model 457 444 431 419 407 270 254 240 226 214 201

Reproduc2ve Number Community 0.23 Hospital 0.3 Funeral 0.2 Overall 0.8


Liberia long term forecasts

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Date Weekly forecast

12/08 270

12/15 255

12/22 240

12/29 227

1/05 213

1/12 202

1/19 190

1/26 179

2/2 169

2/9 160

2/16 142

2/23 127


Sierra Leone – County Data

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Sierra Leone infec2on rate

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Sierra Leone Forecast

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10/06 to

10/12

10/13 to

10/19

10/20 to

10/26

10/27 to

11/02

11/03 to

11/09

11/10 to

11/16

11/17 to

11/23

11/24 to

11/30

12/01 to

12/07

12/08 to

12/14

12/15 to

12/21

12/22 to

12/28

Reported 468 461 454 580 480 684 643 577 598 621

Forecast original 566 690 841 1025 1250 1523 1856

Forecast change txm 430 524 513 543 566 588 612 636 660 713 740

35% of cases are hospitalized

ReproducMve Number Community 0.8 Hospital 0.3 Funeral 0.1 Overall 1.1


SL longer term forecast

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Sierra Leone – Newer Model fit – Weekly Incidence Date Weekly forecast

12/08 660

12/15 686

12/22 713

12/29 740

1/05 769

1/12 799

1/19 830

1/26 862

2/2 895

2/9 929

2/16 965

2/23 1002


Sierra Leone -‐ Prevalence

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Date People in H + I

12/08 898

12/15 932

12/22 968

12/29 1006

1/05 1045

1/12 1085

1/19 1128

1/26 1171

2/2 1216

2/9 1263

2/16 1312

2/23 1363


Guinea Forecasts

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40% of cases are hospitalized

ReproducMve Number Community 0.7 Hospital 0.1 Funeral 0.1 Overall 0.9

10/09 to

10/15

10/16 to

10/19

10/23to

10/29

10/30to

11/05

11/06 to

11/12

11/13 to

11/19

11/20 to

11/26

11/27 to

12/03

12/04 to

12/10

12/11 to

12/17

12/18 to

12/24

12/25 to

1/01

Reported 175 129 143 12 136 121 142 69 86 55

Forecast 118 118 115 112 109 106 103 87 84 80 77 77


Guinea – longer term forecast

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Date Weekly forecast

12/08 81

12/15 78

12/22 75

12/29 72

1/05 69

1/12 66

1/19 63

1/26 60

2/2 58

2/9 55

2/16 53

2/23 51


Guinea Prevalence

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Date People in H+I

12/08 98

12/15 94

12/22 90

12/29 86

1/05 82

1/12 79

1/19 76

1/26 72

2/2 69

2/9 66

2/16 64

2/23 61


Agent-‐based Calibra2on

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Incorporates behavioral change around Sept 21


Imported into SIBEL

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Base Case •  Bias towards household members –  70% less likely to transmit outside the household

•  Hospital Isola2on –  41% isolated with 80% efficacy

•  Proper Burial –  56% buried with 80% reduc2on

•  Behavioral Change – late Sept –  45% reduc2on in effec2ve contacts

•  Behavioral Change – late Oct –  25% reduc2on in effec2ve contacts

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Base Case -‐ Overview

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Mean of mul2ple Replicates


Base Case -‐ Single Replicate

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Prelim Vaccine Study -‐ Design

•  Random: Applying large amount to popula2on at random – 200k, 600k, 1M – Efficacy 20%, 50%, 80% –  Instantaneously or administered 10k per day

•  Targeted: Apply to those with contact with a case before symptoms – Same quan22es as above

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Prelim Vaccine Study -‐ Analysis

•  Compare future cases over 2 months aner vaccina2on

•  Focusing on Liberia with small number of forecasted future cases limits interpretability

•  Study revealed new requirement for simula2on engine

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Prelim Vax Study Results

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Doses Efficacy Mean future

cases %

ReducMon 200k 20 246.36 19% 200k 50 297.36 2% 200k 80 273.92 10% 600k 20 288.52 5% 600k 50 208.72 31% 600k 80 207.8 32% 1000k 20 252.36 17% 1000k 50 175.28 42% 1000k 80 87.08 71%

Random with Instantaneous ApplicaMon


Agent-‐Based Model Next Steps

•  Re-‐run full study with updated simula2on engine

•  Analyze transmission tree impact

•  Calibrate Sierra Leone – A.empt geographic spread – Run similar prelim study

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POPULATION CONSTRUCTION DETAILS

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Four versions of the Liberia contact network: §  Base version: LBR-‐base: ini2al version constructed using the base pipeline

§  Long distance travel: LBR-‐ldt: base version augmented with links corresponding to contacts arising through travel using FlowMinder data.

§  LBR-‐2-‐group and LBR-‐9-‐group: LBR-‐2gp and LBR-‐9gp: Versions based on the Liberia Labor Force Survey.

Synthe2c popula2ons: Liberia


Why 4 versions? §  LBR-‐base: first version constructed with data that we had found in ini2al data search

§  LBR-‐ldt: the base version construc2on methodology is improved through addi2on of contacts corresponding to long distance travel. FlowMinder data was used to es2mate such contacts and the social contact network of the base version was augmented accordingly.



Why 4 versions (con2nued)? §  LBR-‐2gp and LBR-‐9gp: Aner the construc2ons of the LIB-‐base and LIB-‐ldt social contact networks, we obtained the Liberia Labor Force Survey from 2010. Demographic informa2on from this survey was used to construct these two addi2onal versions that are more closely calibrated against the reported, aggregated 2me-‐use data of this survey. The two versions (2gp/9gp) differ in the number of demographic sub-‐groups (2 and 9) that were used in the calibra2on.

Overall significance: data-‐responsiveness of pipeline; calibra2on, verifica2on and valida2on.



Networks and Measures Country ID |P| n_H n_W n_S n_C n_ALiberia LBR-‐v1 4,092,310 844,066 88,016 5,477 20 22,202,262Liberia LBR-‐2gp 4,092,310 844,066 85,395 5,498 25 22,502,300Liberia LBR-‐9gp 4,092,310 844,066 85,395 5,498 25 20,144,766Liberia LBR-‐ldt 4,092,310 844,066 88,016 5,477 20 22,202,262

Popula2on |P| |V| |E| d_min d_max bar{d} lambda_1 lambda_2 LBR-‐v1 4,092,310 4,084,569 84,789,847 0 249 41.52 125.48 125.34 LBR-‐2gp 4,092,310 4,077,272 87,255,911 0 254 42.8 126.83 126.41 LBR-‐9gp 4,092,310 4,077,426 78,830,017 0 250 38.67 111.97 111.93 LBR-‐ldt 4,092,310 4,079,500 146,386,825 0 721 71.77 202.99 195.47

Popula2on n_C |V_max| r D n_T bar{c} LBR-‐v1 14,073 4,051,099 0.992 18 720,629,723 0.59 LBR-‐2gp 10,106 4,053,906 0.994 16 824,000,000 0.59 LBR-‐9gp 10,014 4,054,303 0.994 17 679,000,000 0.59 LBR-‐ldt 3,611 4,071,515 0.998 11 1.53E+09 0.47


Network Construc2on and Network Measures


LBR-‐base

0 0.005

0.01 0.015

0.02 0.025

0.03 0.035

0.04 0.045

0.05

0 50 100 150 200 250

Liberia


SITUATION ASSESSMENT TOOL

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The Problem Road condi2ons in southern Africa are variable and severe.

In order to win the fight against Ebola, it will be necessary to transport medical supplies and pa2ents as efficiently as possible.


The Solu2on Eyes on the Ground: a web-‐based tool for tracking road condi2ons. Witnesses report road condi2ons as they encounter them Travelers can then use recent and historical informa2on to plan the best route to help.


Future Enhancements


Future Reports

DesMnaMon Last Reported

Travel Time

Traffic CondiMons

Road CondiMons

Comments

Bopolu (Gbarpolu)

2014-‐12-‐11

160 Light Passable

Yangaryah (Gbarpolu)

2014-‐12-‐14

190 Heavy Passable

Mecca (Bomi) 2014-‐12-‐10

210 Medium Passable

Tubmanburg (Bomi)

2014-‐12-‐08

240 Medium Passable 4-‐wheel drive

Gbah Jakeh (Bomi)

2014-‐11-‐03

280 Medium Passable

Parker Cornor (Montserrado)

2014-‐12-‐01

300 Heavy Passable

Sinje (Grand Cape Mount)

2014-‐11-‐30

Impassable


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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