modeling the ebola outbreak in west africa, december 22nd 2014 update

DRAFT – Not for a.ribu2on or distribu2on DRAFT – Not for a.ribu2on or distribu2on

Modeling the Ebola Outbreak in West Africa, 2014

December 22nd 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-‐131

DRAFT – Not for a.ribu2on or distribu2on

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 2,416 1,525 Liberia 7,830 3,290 Sierra Leone 8,759 2085 Total 19,005 6,900


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 362 185 187 156 369 172 -‐-‐

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 468 461 454 580 480 684 643 577 598 621 582

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 55126 53

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 Model Sierra Leone Calibra2on

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Incorporates behavioral change around Oct 15th


Imported into SIBEL

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Sierra Leone Base Case

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

•  Hospital Isola2on – 57% isolated with 82% efficacy

•  Proper Burial – 58% buried with 77% reduc2on

•  Behavioral Change – mid Oct – 16% reduc2on in effec2ve contacts

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Agent-‐Based Model Next Steps

•  Targeted Vaccines – Debug

•  Sierra Leone upda2ng – Na2onal spread calibrated – Work on Geographic spread – Run similar prelim study

•  Genera2on of Synthe2c disease spread as public resource for designing vax trials and campaigns

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FORECAST COMPARISON TOOL EpiViewer

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EpiViewer

•  Portal for exploring, sharing, comparing, and dissemina2ng forecasts of infec2ous diseases

•  Currently set up for Ebola Forecasts •  Intended as a community tool – Hosted on public facing URL (once finalized) – Offers simple controls for exploring the data – Allows independent uploading by 3rd par2es to facilitate sharing, as well as consolidated downloading of data

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EpiViewer – Explore Data

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EpiViewer – Compare Forecasts

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EpiViewer – Upload Data

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EpiViewer – Download All Data

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Columbia_Liberia_MW_11-‐09.csv Columbia_Liberia_MW_11-‐16.csv Columbia_Liberia_MW_11-‐23.csv Columbia_Liberia_MW_11-‐30.csv Columbia_SL_MW_10-‐05.csv Columbia_SL_MW_10-‐12.csv Columbia_SL_MW_10-‐19.txt Columbia_SL_MW_10-‐26.csv Columbia_SL_MW_11-‐02.csv HealthMap_Liberia_Aug.31_MW HealthMap_Liberia_Sept.07_MW HealthMap_Liberia_Sept.14_MW HealthMap_Liberia_Sept.21_MW HealthMap_SL_Aug.17_MW HealthMap_SL_Aug.31_MW Mobs_Guinea_10-‐01_MW.txt MoBS_Liberia_Oct.1_MW.csv MoBS_SL_10-‐1_MW.csv MoBS_SL_11-‐1_MW.csv MoBS_SL_11-‐10_MW.csv NDDSL_SL_Dec16.csv NDSSL_SL_06-‐07_MW.csv NDSSL_SL_08-‐09_MW.csv NDSSL_SL_10-‐11_MW.csv Sur_SL_06-‐07.csv Sur_SL_08-‐09.csv Sur_SL_10-‐11.csv

Downloads a zip file Containing all data


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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modeling the ebola outbreak in west africa, december 22nd 2014 update

Science

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forecast original

paula stretz

youngyoun chungbaek

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