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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
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, 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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
Liberia – Case Loca2ons
4
DRAFT – Not for a.ribu2on or distribu2on
Liberia infec2on rate
5
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
Sierra Leone infec2on rate
9
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
80e_50c -‐ replicate 2
80e_70c -‐ replicate 2
80e_90c -‐ replicate 20
50e_30c -‐ replicate 12
50e_50c -‐ replicate 15
50e_70c -‐ replicate 18
50e_90c -‐ replicate 13
DRAFT – Not for a.ribu2on or distribu2on 18
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
DRAFT – Not for a.ribu2on or distribu2on 19
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%
DRAFT – Not for a.ribu2on or distribu2on 20
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
DRAFT – Not for a.ribu2on or distribu2on 21
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%
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
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
DRAFT – Not for a.ribu2on or distribu2on
Stochas2c Simula2ons
• Bayesian approach, analyze all replicates, consider how well observed fits in, use this to es2mate uncertainty and assign weights for outcome analysis
26