View
93
Download
2
Category
Preview:
Citation preview
DRAFT – Not for a.ribu2on or distribu2on
Modeling the Ebola Outbreak in West Africa, 2014
November 25th Update
Bryan Lewis PhD, MPH (blewis@vbi.vt.edu) 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-‐129
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
2
DRAFT – Not for a.ribu2on or distribu2on
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.
3
Cases Deaths Guinea 2047 1214 Liberia 6909 2964 Sierra Leone 6190 1510 Total 15,168 5696
DRAFT – Not for a.ribu2on or distribu2on
Liberia – Case Loca2ons
4
DRAFT – Not for a.ribu2on or distribu2on
Comparison new WHO vs. MoH
5
DRAFT – Not for a.ribu2on or distribu2on
Liberia – County Case Incidence
6
DRAFT – Not for a.ribu2on or distribu2on
Liberia Forecast – New Model
7
9/16 to
9/21
9/22 to
9/28
9/29 to
10/5
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
12/1 to
12/7
Reported 560 416 261 298 446 1604* 227 298 -‐-‐ -‐-‐
Reported back log adjusted 657 549 691 490
Newer model 660 496 512 508 497 483 469 457 444 431 419 407
Reproduc2ve Number Community 0.3 Hospital 0.3 Funeral 0.3 Overall 0.9
DRAFT – Not for a.ribu2on or distribu2on
Liberia Longer-‐term forecast
8
2014-‐09-‐21 660 2014-‐09-‐28 496 2014-‐10-‐05 512 2014-‐10-‐12 508 2014-‐10-‐19 497 2014-‐10-‐26 483 2014-‐11-‐02 470 2014-‐11-‐09 457 2014-‐11-‐16 444 2014-‐11-‐23 431 2014-‐11-‐30 419 2014-‐12-‐07 407 2014-‐12-‐14 395 2014-‐12-‐21 384 2014-‐12-‐28 373 2015-‐01-‐04 362 2015-‐01-‐11 352 2015-‐01-‐18 342 2015-‐01-‐25 332
Liberia – Newer Model fit – Weekly Incidence
Projec2ons this far into future can’t take into account the behavioral factors that drive Ebola transmission
DRAFT – Not for a.ribu2on or distribu2on
Prevalence of Cases – New model
9
Date People in H+I 9/14/14 695 9/20/14 887 9/27/14 1103 10/4/14 1139 10/11/14 1124 10/18/14 1097 10/25/14 1068 11/1/14 1038 11/8/14 1009 11/15/14 980 11/22/14 952 11/29/14 925 12/6/14 899 12/13/14 873 12/20/14 848 12/27/14 824
DRAFT – Not for a.ribu2on or distribu2on
Sierra Leone – County Data
10
DRAFT – Not for a.ribu2on or distribu2on
Comparison new WHO vs. MoH
11
DRAFT – Not for a.ribu2on or distribu2on
Sierra Leone Forecasts
12
9/6 to
9/14
9/14 to
9/21
9/22 to
9/28
9/29 to
10/05
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
Reported 246 285 377 467 468 454 494 486 580 -‐-‐ -‐-‐
Forecast original 256 312 380 464 566 690 841 1025 1250 1523 1856
Forecast change txm 430 524 561 590 619 650
35% of cases are hospitalized
ReproducMve Number Community 1.10 Hospital 0.37 Funeral 0.15 Overall 1.63
DRAFT – Not for a.ribu2on or distribu2on
SL longer term forecast
13
Sierra Leone – Newer Model fit – Weekly Incidence
2014-‐10-‐19 431 2014-‐10-‐26 524 2014-‐11-‐02 561 2014-‐11-‐09 591 2014-‐11-‐16 620 2014-‐11-‐23 650 2014-‐11-‐30 682 2014-‐12-‐07 715 2014-‐12-‐14 749 2014-‐12-‐21 786 2014-‐12-‐28 824
DRAFT – Not for a.ribu2on or distribu2on
Sierra Leone -‐ Prevalence
14
Date People in H+I
9/15/14 253 9/22/14 309 9/29/14 377 10/6/14 460 10/13/14 560 10/19/14 657 10/26/14 715 11/2/14 754 11/9/14 791 11/16/14 830 11/23/14 870 11/30/14 912 12/7/14 957 12/14/14 1003 12/21/14 1052 12/28/14 1103
DRAFT – Not for a.ribu2on or distribu2on
Agent-‐based Model Studies • Transmission Tree analysis to inform – Exposure source over genera2ons and 2me – Exposure source over chains of transmission – Use to es2mate under-‐repor2ng
• Vaccine studies – Vaccine trials:
• Es2ma2on of popula2on size needed • Es2ma2on of vaccine effec2veness
– Logis2cs of a Vaccine campaign: • Priori2za2on of target popula2ons • Sensi2vi2es of compliance levels and rates of admin
15
DRAFT – Not for a.ribu2on or distribu2on
Vaccina2on Studies – Trials
• Step-‐wedge requires good es2mates of transmission in geographically dis2nct areas – Spa2al spread calibrated model (in progress) can assist with quan2fying these levels
• Es2mate effec2veness by simula2ng what transmission would have been without vaccine vs. with vaccine of differing efficacies – Can use informa2on about known levels of admin
16
DRAFT – Not for a.ribu2on or distribu2on
Vaccina2on Campaigns
• Rollout of ini2al batches: – 30K in Jan -‐ for trial? – 30K more in Feb (?)
• Targe2ng of high risk individuals 1. Healthcare workers and burial teams 2. Contacts of infected individuals 3. High risk contacts of these contacts 4. Those in high transmission areas
17
DRAFT – Not for a.ribu2on or distribu2on
Vaccina2on Study
• Parameters to explore: – Administra2on of vaccine
• Rate of administra2on • Loca2ons, focused on one geographic area or many
– Compliance amongst different risk groups
• Sensi2vity analysis will compare parametric sweeps of the above
18
DRAFT – Not for a.ribu2on or distribu2on
Transmission Tree
19
Home 69% orange Work 12% blue School 17% green College 1% red
Nodes colored by greyscale number of transmissions (1 to 13)
DRAFT – Not for a.ribu2on or distribu2on
Transmission Trees
20
X-‐axis is Days Red is home Blue is non-‐home
DRAFT – Not for a.ribu2on or distribu2on
Transmission Trees
21
Layout to show branches Color is Day (blue=early, red=late) Edge color is ac2vity type
DRAFT – Not for a.ribu2on or distribu2on
APPENDIX Suppor2ng material describing model structure, and addi2onal results
22
DRAFT – Not for a.ribu2on or distribu2on
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.
23
DRAFT – Not for a.ribu2on or distribu2on
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.
24
DRAFT – Not for a.ribu2on or distribu2on
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
25
DRAFT – Not for a.ribu2on or distribu2on
Parameters of two historical outbreaks
26
DRAFT – Not for a.ribu2on or distribu2on
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
27
DRAFT – Not for a.ribu2on or distribu2on
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
28
DRAFT – Not for a.ribu2on or distribu2on
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
29
DRAFT – Not for a.ribu2on or distribu2on
Model parameters
30
Sierra&Leonealpha 0.1beta_F 0.111104beta_H 0.079541beta_I 0.128054dx 0.196928gamma_I 0.05gamma_d 0.096332gamma_f 0.222274gamma_h 0.242567delta_1 0.75delta_2 0.75
Liberiaalpha 0.083beta_F 0.489256beta_H 0.062036beta_I 0.1595dx 0.2gamma_I 0.066667gamma_d 0.075121gamma_f 0.496443gamma_h 0.308899delta_1 0.5delta_2 0.5
All Countries Combined
DRAFT – Not for a.ribu2on or distribu2on
Learning from Lofa
31
Model fit to Lofa case series up Aug 18th (green) then from Aug 19 – Oct 21 (blue), compared with real data (red)
DRAFT – Not for a.ribu2on or distribu2on
Learning from Lofa
32
Model fit to Lofa case with a change in behaviors resul2ng in reduced transmission sta2ng mid-‐Aug (blue), compared with observed data (green)
DRAFT – Not for a.ribu2on or distribu2on
Learning from Lofa
33
Model fit to Liberian case data up to Sept 20th (current model in blue), reduc2on in transmissions observed in Lofa applied from Sept 21st on (green), and observed cases (red)
DRAFT – Not for a.ribu2on or distribu2on
Learning from Lofa
34
Model fit to Liberia case with a change in behaviors resul2ng in reduced transmission sta2ng Sept 21st (green), compared with observed data (blue)
DRAFT – Not for a.ribu2on or distribu2on
Simula2on Comparison – spread from Lofa
35
Cases per 100k popula2on
Mean simula2on Normal Travel Ministry of Health Data
DRAFT – Not for a.ribu2on or distribu2on
Simula2on Comparison – Rainy Season Travel
36
Cases per 100k popula2on
Mean simula2on Rainy Travel Ministry of Health Data
DRAFT – Not for a.ribu2on or distribu2on
Simula2on Comparison – spread from Lofa
37
Total Cases
Single Simula2on result – Normal Travel Ministry of Health Data
DRAFT – Not for a.ribu2on or distribu2on
Simula2on Comparison – spread from Lofa
38
Total Cases
Single Simula2on result – Rainy Travel Ministry of Health Data
Recommended