modeling the ebola outbreak in west africa, february 10th 2015 update

DRAFT – Not for a.ribu2on or distribu2on

Modeling the Ebola Outbreak in West Africa, 2014

February 10th 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 #15-‐015


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, Bill Marmagas Students: S.M. Arifuzzaman, Aditya Agashe, Vivek Akupatni, Caitlin Rivers, Pyrros Telionis, Jessie Gunter, Elizabeth Musser, James Schli., Youssef Jemia, Margaret Carolan, Bryan Kaperick, Warner Rose, Kara Harrison 2


Currently Used Data (as of Feb 4th, 2014)

●  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.

3

Cases Deaths Guinea 2,975 1,944 Liberia 8,745 3,746 Sierra Leone 10,740 3,276 Total 22,724 8,981


Liberia – Case Loca2ons

4


Liberia infec2on rate

5


Liberia Forecast

6

12/23 -‐

1/01

1/02 -‐

1/08

1/09 -‐

1/15

01/16 -‐

1/22

1/23 -‐

2/01

2/02 -‐

2/08

2/09 -‐

2/16

Reported 190 163 107 130 197

Updated model 187 174 162 151 141 131 122

Reproduc2ve Number Community 0.3 Hospital 0.3 Funeral 0.2 Overall 0.8


Liberia long term forecasts

7

Date Weekly forecast

2/9 122

2/16 114

2/23 106

3/02 99

3/09 92

3/16 86

3/23 80


Liberia-‐ Prevalence

8

Date People in H + I

2/2 331

2/9 308

2/16 288

2/23 268

3/02 250

3/09 233


Sierra Leone infec2on rate

9


Sierra Leone Forecast

10

35% of cases are hospitalized

ReproducRve Number Community 0.7 Hospital 0.2 Funeral 0.1 Overall 1.0

12/28 -‐

1/04

1/05 -‐

1/11

1/12 -‐

1/18

1/19 -‐

1/25

1/26 -‐

2/01

2/02 -‐

2/08

02/09 -‐

02/16

Reported 334 260 212 129 146

Updated model 317 290 267 244 224 205 188


SL longer term forecast

11

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

2/2 224

2/9 205

2/16 188

2/23 172

3/02 158

3/09 145

3/16 132


Sierra Leone -‐ Prevalence

12

Date People in H + I

1/26 448

2/2 411

2/9 376

2/16 345

2/23 316

3/02 289

3/09 265


Guinea Forecasts

13

40% of cases are hospitalized

ReproducRve Number Community 0.25 Hospital 0.09 Funeral 0.01 Overall 0.36

12/22 -‐

12/28

12/29 -‐

1/04

1/05 -‐

1/11

1/12 -‐

1/18

1/19 -‐

1/25

1/26 -‐

2/01

2/02 -‐

2/08

2/09 -‐

2/15

Reported 100 45 30 46 44 38

Updated model 94 91 77 61 45 33 24 18


Guinea – longer term forecast

14

Date Weekly forecast

1/26 45

2/2 33

2/9 24

2/16 18*

2/23 13*

3/02 9*

* too small for reliable forecas2ng


Guinea Prevalence

15

Date People in H+I

1/26 95

2/2 93

2/9 90

2/16 88

2/23 86

3/02 83

3/09 81


Agent-‐based Model Progress •  Review: Sensi2vity to compliance levels vaccine campaign study

•  Review: Stepped-‐Wedge study design being considered by CDC details from Ebola Modeling conference

•  Review: Analy2c methods developed for comparison of stochas2c simula2on results

•  Update: Calibra2on for SL updated •  Update: Study design for future outbreak planning •  Ongoing: Stochas2c ex2nc2on / 2me to zero

16


Calibra2on of Simulated Vaccine Campaigns

17

0

5000

10000

15000

20000

25000

55

62

69

76

83

90

97

104

111

118

125

132

139

146

153

160

167

174

181

188

195

202

209

216

223

230

237

244

251

258

265

272

279

286

293

300

307

314

321

328

335

342

349

356

363

370

Model 80%e 30%c Model 80%e 90%c Model 50%e 30%c Model 50%e 90%c MoH Data

DRAFT – Not for a.ribu2on or distribu2on 18

% Change in Infec2ons Following Vaccina2on Beginning Feb 1 (30k Doses)

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Baseline -‐ replicate 11

80e_30c -‐ replicate 15









30k Doses – Percent Reduc2on by Efficacy and Compliance

Compliance

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

90% 70% 50% 30%

80% Efficacy

50% Efficacy


30k Doses -‐ Cumula2ve Infec2ons using the Mean of most relevant replicates

% InfecRons Occurring Between Feb-‐1 and Apr-‐1

% ReducRon

Compliance

80% Efficacy

50% Efficacy

80% Efficacy

50% Efficacy

90%

27.54%

32.38%

30.55%

18.34%

70%

31.22%

34.78%

21.25%

12.28%

50%

32.62%

35.07%

17.73%

11.54%

30%

34.88%

35.83%

12.03%

9.62%

Baseline

39.65%


Compliance

300k Doses – Percent Reduc2on by Efficacy and Compliance

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

90% 70% 50% 30%

80% Efficacy

50% Efficacy


300k Doses -‐ Cumula2ve Infec2ons using the Mean of most relevant replicates

% InfecRons Occurring Between Feb-‐1 and Apr-‐1

% ReducRon in Cases A[er Feb-‐1

Compliance

80% Efficacy

50% Efficacy

80% Efficacy

50% Efficacy

90%

26.47%

30.29%

33.23%

23.59%

70%

29.61%

32.34%

25.33%

18.42%

50%

31.04%

32.41%

21.71%

18.24%

30%

32.31%

35.31%

18.49%

10.93%

Baseline

39.65%


Vaccine Trial Design •  Stepped wedge: Enroll and follow-‐up all, vaccinate over 2me, compare rates vax and no-‐vax cohorts

23

Weeks a[er start of trail Cluster doses 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 1 ~333 2 ~333 3 ~333 4 ~333 5 ~333 6 ~333 7 ~333 8 ~333 9 ~333 10 ~333 11 ~333 12 ~333 13 ~333 14 ~333 15 ~333 16 ~333 17 ~333 18 ~333

Vaccinated but not seroconverted Compare rates among enrolled but not vaccinated vs. seroconverted vaccinees

Vaccinated and protected Enrolled but not vaccinated Blue box follow up 2me for analysis of efficacy


Stepped Wedge Design

•  Key components – Assume weeks have similar hazard of infec2on across clusters (or classes of clusters)

– Cox Propor2onal Hazards Risk can be used to assess efficacy

•  Under considera2on for CDC-‐run trial – Current assessment is its too underpowered, when there is declining incidence

– Leaning towards a different cluster based design

24


Stochas2c Simula2ons

•  CNIMS simula2ons include a lot structure to capture the inherent stochas2city of the real world

25

Distribu2on of 1000 replicates of Liberian Ebola epidemics


Stochas2c Simula2ons •  Capturing this fundamental behavior of complex systems

is important –  Used to es2mate bounds on “possible worlds” –  Provides rich distribu2ons of outcomes from interven2ons for sta2s2cal analysis

•  Need to apply different techniques for analysis –  Ques2ons about the outcome of ac2ons given the system is in par2cular state requires iden2fica2on of individual realiza2ons of the simula2on that fit “criteria” or combines them appropriately

–  Example: Given we have an outbreak like what has happened in Sierra Leone (to the degree we’ve been able to observe it accurately) what would a vaccine campaign do? •  Filter realiza2ons most like observed data •  Discount

26


Stochas2c Simula2ons

•  Bayesian approach, analyze all replicates, consider how well observed fits in, use this to es2mate uncertainty and assign weights for outcome analysis

27


Agent-‐based Calibra2on •  Updated for Sierra Leone

28


Incorpora2ng Uncertainty

29

•  Different “fi.ed” parameter sets yield different levels of stochas2c variance

•  Different “fi.ed” parameter sets yield different levels of stochas2c variance


Ebola Outbreak Planning

•  What levels of vaccine are needed and when to prevent future outbreaks?

•  Assump2ons – One of the vaccine candidates will be effec2ve and safe enough to be used

– Current outbreak is a “worst case” –  Ini2al control is a.empted with classic isola2on and treatment

30


Planning Study Design

•  Scenario: – Use models fit to current outbreaks in all 3 countries

•  Interven2ons: –  Vaccine doses: April 1K, July 30K (more / less?)

•  Metrics: – How much is needed to stop outbreak –  Explore sensi2vi2es using a ring-‐vaccina2on strategy

•  Case iden2fica2on •  Efficacy of vaccine •  Contact finding / compliance

31


•  Allows users to compare and filter on mul2ple epicurves

•  Visualizes both incidence

data and cumula2ve data along with uncertainty bounds

Compare forecasts in EpiViewer


EpiViewer – data filter and features

33

•  Easy upload and download mechanic for acquiring and adding plot data

•  Data filter plots epicurves

based on: region, category of curves, surveillance data, forecasts, model output, name of the curves

•  Zoomable date selec2on for specific ranges on concurrent plots


EpiViewer – Animated plots

•  Added anima2on mode for be.er visual comparison

34

modeling the ebola outbreak in west africa, february 10th 2015 update

Sales

distribu2on liberia

distribu2on guinea forecasts

moh sierra leone

moh liberia

updated model

date people

reproduc2ve number community

cases deaths guinea