modeling the ebola outbreak in west africa, january 27th 2015 update

DRAFT – Not for a.ribu2on or distribu2on

Modeling the Ebola Outbreak in West Africa, 2014

January 27th 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-‐013


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, Elisabeth Musser, James Schli., Youssef Jemia, Margaret Carolan, Bryan Kaperick, Warner Rose, Kara Harrison 2


Currently Used Data (as of Jan 23th, 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 2,871 1,814 Liberia 8,462 3,538 Sierra Leone 10,340 3,062 Total 21,673 8,414


Liberia – Case Loca2ons

4


Liberia infec2on rate

5


Liberia Forecast

6

12/29 to 1/04

1/05 to 1/11

1/12 to 1/18

1/19-‐1/25

1/26-‐2/01

1/27-‐2/01

2/02 -‐

2/08

2/09 -‐

2/15

Reported 131 116

Newer model

174 162 151 141 131 122 114 106

Reproduc2ve Number Community 0.3 Hospital 0.3 Funeral 0.2 Overall 0.8


Liberia long term forecasts

7

Date Weekly forecast

2/2 131

2/9 122

2/16 114

2/23 106

3/02 99

3/09 92

3/16 86

3/23 80

3/30 75


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

1/05 to 1/11

1/12 to 1/18

1/19 -‐

1/25

1/26 -‐

2/01

2/02 -‐

2/08

2/09 -‐

2/15

2/16 -‐

2/22

2/23 -‐

3/01

Reported 491

Newer model 427 414 402 391 380 358 348 328


SL longer term forecast

11

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

1/26 402

2/2 391

2/9 380

2/16 369

2/23 358

3/02 348

3/09 338


Sierra Leone -‐ Prevalence

12

Date People in H + I

1/26 882

2/2 900

2/9 918

2/16 937

2/23 995

3/02 1015

3/09 1034


Guinea Forecasts

13

40% of cases are hospitalized

ReproducRve Number Community 0.7 Hospital 0.1 Funeral 0.1 Overall 0.9

12/29 to 1/04

1/05 to

1/11

1/12 to

1/18

1/19 -‐

1/25

1/26 -‐

2/01

2/02 -‐

2/08

2/09 -‐

2/15

2/16 -‐

2/23

Reported 106 62 23

Newer model

91 89 86 84 82 80 78 76


Guinea – longer term forecast

14

Date Weekly forecast

1/26 82

2/2 80

2/9 78

2/16 76

2/23 74

3/02 72


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

•  Sensi2vity to compliance with vaccine assessed •  Stepped-‐Wedge study design being considered by CDC details from Ebola Modeling conference

•  Analy2c methods developed for comparison of stochas2c simula2on results

16

DRAFT – Not for a.ribu2on or distribu2on 17

% 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

22

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

23


Stochas2c Simula2ons

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

24

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

25


Stochas2c Simula2ons

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

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modeling the ebola outbreak in west africa, january 27th 2015 update

Science

date peopleinh

reproducrvenumber community

reproduc2venumber community

newer model

paula stretz

youngyoun chungbaek

maureen lawrencekuether

richard beckman