Modeling the Ebola Outbreak in West Africa, October 21st 2014 update

DRAFT – Not for a.ribu2on or distribu2on

Modeling the Ebola Outbreak in West Africa, 2014

Oct 21st 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-‐112


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 1519 862 Liberia 4068 2484 Nigeria 22 8 Sierra Leone 3624 1200 Total 9233 4554


Epi Notes

•  US hospitals are being built, some delays due to rainy season NPR

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•  Good news: Nigeria Ebola free!! Time •  Good news: Lofa, Liberia having success WHO •  Bad news: Surge in cases in Conakry, Guinea MSF •  Transmission route unclear for Nancy Writebol (interes2ng interview) Science Mag


Liberia – Case Loca2ons

4


Liberia – County Case Incidence

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Liberia – County Case Propor2ons

6

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

6/10/14 6/30/14 7/20/14 8/9/14 8/29/14 9/18/14 10/8/14 10/28/14

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

Nimba County

River Gee County

RiverCess County

Sinoe County

Lofa

Margibi

Bomi

Montserrado


Liberia – Contact Tracing

7


Liberia – Contact tracing

8


Liberia – Case Confirma2on

9

Gives an idea of the rela2ve performance and case management in the different coun2es. Decreasing rates combined with number of lost and not seen contacts (previous slide), indicate the response efforts are overwhelmed.


Liberia Forecasts

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8/9/08-‐9/14

9/15– 9/21

9/22– 9/28

9/29 – 10/05

10/06 – 10/12

10/13-‐10/19

10/20-‐10/26

Reported 639 560 416 261 119 -‐-‐ -‐-‐

Forecast 697 927 1232 1636 2172 2883 3825

Reproduc2ve Number Community 1.3 Hospital 0.4 Funeral 0.5 Overall 2.2

52% of Infected are hospitalized


Prevalence of Cases

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Week People in H+I

9/28/2014 1228

10/05/2014 1631

10/12/2014 2167

10/19/2014 2878

10/26/2014 3821

11/02/2014 5071

11/16/2014 8911


Sierra Leone Forecasts

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41% of cases are hospitalized

9/6-‐9/14

9/14-‐9/21

9/22 – 9/28

9/29-‐ 10/05

10/06– 10/12

10/13-‐10/19

10/20-‐10/26

Reported 246 285 377 467 468 372

Forecast 413 512 635 786 973 1205 1491


Prevalence in SL

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Week People in H+I

9/28/2014 668

10/05/2014 828

10/12/2014 1026

10/19/2014 1271

10/26/2014 1573

11/02/2014 1947

11/16/2014 2978


All Countries Forecasts

14

8/18 – 8/24

8/25 – 8/31

9/1– 9/7

9/8 – 9/14

9/15-‐ 9/21

9/22 – 9/28

9/29 – 10/5

10/6 -‐10/12

10/13-‐10/19

10/20-‐10/26

Actual 559 783 681 959 917 915 904 917

Forecast 483 578 693 830 994 1191 1426 1426 1708 2045

rI: 1.1 rH:0.4 rF:0.3 Overall:1.7


Experiments & Research

•  Exploring the reported case decline in Liberia •  Quick look at “WHO plan” 70% hospitalized, 70% safely buried in 60 days

•  Analyzing / gathering US HCW exposure and risk in case we need to address more US spread scenarios

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Liberia underrepor2ng

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Control with 70% Hospitalized?

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Star2ng 1 October, 70 percent of cases are diagnosed and treated, the efficacy of that care and the safety of burial for those who die is subject to the exis2ng efficacy of the healthcare system.

Liberia -‐ 100 day projec@on


Agent-‐based Model Progress

•  Construc2on of regional travel dynamic social network

•  Framework for auto-‐calibra2on built •  New version of SIBEL deployed –  Enables all trained analysts to run Ebola simula2ons without “behind the scenes” manipula2on

–  Auto-‐modifica2on possible for more advanced changes

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Regional Travel -‐ Liberia •  Mobility data comes from flowminder.org

–  Probability Matrix of county to county trips by week (15x15) –  Number of trips probably high, ra2os be.er –  Es2mates available for several model fits –  Data converted to daily probabili2es

•  Method: Make dynamic schedules for EpiSimdemics –  Each person has a home county based on home loca2on –  Each person is matched with a person in each non-‐home county, based on gender and age bin

–  For each person and non-‐home county, a new schedule is created that shadows the schedule of the matched person

–  A scenario file is created that contains rules for each source/des2na2on pair (15 x 14 = 210 for Liberia)

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Regional Travel -‐ Example

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# Travel from Grand_Kru (2042) to Maryland (2082) with prob 0.008036427trigger repeatable person.County = 2042 and person.isTraveling = -1 apply travel_to_2082 with prob=0.008036427

intervention travel_to_2008 set person.isTraveling = 2008 set person.daysLeft = 3 set tripsTo2008++ set traveling++ set trips++ schedule county2008 1

# return from travelintervention return unschedule 1 set person.isTraveling = -1 set person.daysLeft = -1 set traveling--

trigger repeatable person.daysLeft > 0 set person.daysLeft—

trigger repeatable person.daysLeft = 1 apply return


Regional Travel -‐ Trips

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100000

100500

101000

101500

102000

102500

103000

103500

104000

104500

105000

10 20 30 40 50 60 70 80 90 100

Tra

velle

rs

Simulation Day

Travelers per day


Regional Travel – Trips

22

0

2000

4000

6000

8000

10000

12000

0 10 20 30 40 50 60 70 80 90 100

Tri

p S

tart

s

Simulation Day

MontserradoMargibi

BomiGrand_Bassa

BongGrand_Cape_Mount

NimbaGbarpolu

River_CessLofa

Grand_GedehMaryland

SinoeRiver_GeeGrand_Kru


Auto-‐Calibra2on of ABM

•  Agent-‐based model is harder to calibrate than compartmental model – More poten2al parameters to tweak – More randomness to outcomes – Longer run-‐2mes

•  Need an automated process to push the model out

23


Auto-‐Calibra2on of ABM

24


SIBEL – New version

25


SIBEL – New features

•  Generic interven2on supports more possible interven2ons

•  Dura2on and logis2cal rates of interven2on added

•  Many more…

26


Agent-‐based Next steps

•  Hospital, ECC, home care kits and their impact at different levels of provision / efficacy –  Constrain to Monrovia for tractability –  Explore use of auto-‐calibra2on to establish a good match to present

–  Explore behavioral changes and details of care (one care giver only at home vs. several, etc.)

•  Calibra2ng Regional Travel to observed spread – Mul2-‐dimensional calibra2on will be challenging – Use more efficient simula2on plauorm (EpiFast)

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APPENDIX Suppor2ng material describing model structure, and addi2onal results

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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.

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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.

30


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

31


Parameters of two historical outbreaks

32


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

33


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

34


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

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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