Modeling the Ebola Outbreak in West Africa, November 25th 2014 update

DRAFT – Not for a.ribu2on or distribu2on

Modeling the Ebola Outbreak in West Africa, 2014

November 25th 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-‐129


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


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


Liberia – Case Loca2ons

4


Comparison new WHO vs. MoH

5


Liberia – County Case Incidence

6


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


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


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


Sierra Leone – County Data

10


Comparison new WHO vs. MoH

11


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


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


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


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


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


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


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


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)


Transmission Trees

20

X-‐axis is Days Red is home Blue is non-‐home


Transmission Trees

21

Layout to show branches Color is Day (blue=early, red=late) Edge color is ac2vity type


APPENDIX Suppor2ng material describing model structure, and addi2onal results

22


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


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


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


Parameters of two historical outbreaks

26


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


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


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


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


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)


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)


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)


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)


Simula2on Comparison – spread from Lofa

35

Cases per 100k popula2on

Mean simula2on Normal Travel Ministry of Health Data


Simula2on Comparison – Rainy Season Travel

36

Cases per 100k popula2on

Mean simula2on Rainy Travel Ministry of Health Data



37

Total Cases

Single Simula2on result – Normal Travel Ministry of Health Data



38

Total Cases

Single Simula2on result – Rainy Travel Ministry of Health Data