Modeling the Ebola Outbreak in West Africa, November 4th 2014 update

DRAFT – Not for a.ribu2on or distribu2on

Modeling the Ebola Outbreak in West Africa, 2014

Nov 4th Update

Bryan Lewis PhD, MPH ([email protected]) Caitlin Rivers MPH, Eric Lofgren PhD, James Schli., Alex Telionis MPH,

Henning Mortveit PhD, Dawen Xie MS, Samarth Swarup PhD, Hannah Chungbaek, Keith Bisset PhD, Maleq Khan PhD, Chris Kuhlman PhD,

Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barre. PhD

Technical Report #14-‐113


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.

2

Cases Deaths Guinea 1906 997 Liberia 6454 2705 Sierra Leone 5235 1500 Total 13,617 5210


Liberia – Case Loca2ons

3


Liberia – County Case Incidence

4


0

0.1

0.2

0.3

0.4

0.5

0.6

5/21/14 6/10/14 6/30/14 7/20/14 8/9/14 8/29/14 9/18/14 10/8/14 10/28/14 11/17/14

Percen

tage of C

ounty Po

pula:o

n (%

)

Date

Percentage of County Popula:on Infected with EVD Bomi County

Bong County

Gbarpolu County

Grand Bassa

Grand Cape Mount Grand Gedeh

Grand Kru

Lofa County

Margibi County

Maryland County

Montserrado County

Liberia – County Case Propor2ons

5


Liberia – Contact Tracing

6


Liberia Forecasts

7

8/9/08 to

9/14

9/15 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

Reported 639 560 416 261 298 446 1604* -‐-‐ -‐-‐

Forecast (classic model)

697 927 1232 1636 2172 2883 3825 5070 6741

Reproduc2ve Number Community 1.3 Hospital 0.4 Funeral 0.5 Overall 2.2

52% of Infected are hospitalized

* Massive increase


Learning from Lofa -‐ Summary

8

Model fit to Lofa case with a change in behaviors resul2ng in reduced transmission sta2ng mid-‐Aug (blue), compared with observed data (green)

Fit reduc2on seen in Lofa

Model fit to Liberia case with a change in behaviors resul2ng in reduced transmission sta2ng Sept 21st (green), compared with observed data (blue)

Apply to Liberia


Liberia Forecast – New Model

9

9/16 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

Reported 560 416 261 298 446 1604* -‐-‐ -‐-‐ -‐-‐

Reported back log adjusted

396 251 245 490

New model 757 603 541 580 598 608 617 625 633

Reproduc2ve Number Community 0.5 Hospital 0.2 Funeral 0.2 Overall 1.0

* Massive increase


Prevalence of Cases – New model

10

Date People in H+I 9/7/14 523 9/14/14 695 9/20/14 887 9/27/14 1051 10/4/14 1119 10/11/14 1152 10/18/14 1174 10/25/14 1192 11/1/14 1208 11/8/14 1224 11/15/14 1239 11/22/14 1255 11/29/14 1271 12/6/14 1288 12/13/14 1304 12/20/14 1320 12/27/14 1337


Sierra Leone – County Data

11


Sierra Leone – Contact A.ack Rate

12


Sierra Leone Forecasts

13

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

Reported 246 285 377 467 468 454 494

Forecast 256 312 380 464 566 690 841 1025 1250

35% of cases are hospitalized

Reproduc:ve Number Community 1.20 Hospital 0.29 Funeral 0.15 Overall 1.63


Prevalence in SL

14

10/6/14 456.6 10/13/14 556.7 10/20/14 678.8 10/27/14 827.5 11/3/14 1008.8 11/10/14 1229.8 11/17/14 1498.9 11/24/14 1826.8 12/1/14 2226.1 12/8/14 2712.2 12/15/14 3303.7 12/22/14 4023.3 12/29/14 4898.1


Experiments and Research

•  US Health care worker Exposure

15


US cases per exposure hour by exposure type

2 / 48 = 0.042

0 / 3432 = 0.0

Transmission probability per triage hour of exposure*

Transmission probability per ICU hour of exposure*

* Assuming that during the triage period HCWs do not u2lize full protec2ve gear and isola2on protocol while wai2ng for Ebola test results.

* Assuming that during the ICU period HCWs do u2lize full protec2ve gear and isola2on protocol while trea2ng Ebola pa2ents.


US overall experience to date 1 transmission for every 1716 exposure hours (71.5 days)

US Healthcare System


Agent-‐based Model Progress

•  Calibra2on progress – Spa2al spread guided by seeding

18


Calibra2on – Spa2al Spread

19


Simula2on Comparison

20

Cases per 100k popula2on

Mean simula2on results Ministry of Health Data


Simula2on Comparison

21

Total Cases

Single Simula2on result Ministry of Health Data


Agent based Next Steps

•  Spa2al spread calibra2on –  Incorporate degraded road network to help guide filng to current data

– Guide with more spa2ally explicit ini2al infected seeds and interven:ons

•  Experiments: –  Impact of hospitals with geo-‐spa2al disease

•  Configura2on s2ll being set up – Vaccina2on campaign effec2veness

•  Framework under development

22


APPENDIX Suppor2ng material describing model structure, and addi2onal results

23


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.

24


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.

25


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

26


Parameters of two historical outbreaks

27


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

28


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

29


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

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Model parameters

31

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

Science

Modeling the Ebola Outbreak in West Africa, November 4th 2014 update