modeling the ebola outbreak in west africa, november 18th 2014 update

DRAFT – Not for a.ribu2on or distribu2on

Modeling the Ebola Outbreak in West Africa, 2014

November 18th 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-‐122


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

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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.

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Cases Deaths Guinea 1919 1166 Liberia 6909 2836 Sierra Leone 5586 1510 Total 14,436 5520


Liberia – Case Loca2ons

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Liberia – County Case Incidence

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Liberia Forecast – Original Model

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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* 227 298

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

* Repor2ng change


Learning from Lofa -‐ Summary

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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 – Prelim New Model

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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

11/17 to

11/23

Reported 560 416 261 298 446 1604* 227 298 -‐-‐ -‐-‐

Reported back log adjusted

396 251 245 490

New model 757 603 541 580 598 608 617 625 633 638

Reproduc2ve Number Community 0.5 Hospital 0.2 Funeral 0.2 Overall 1.0


Prevalence of Cases – New model

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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

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Sierra Leone – Contact A.ack Rate

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Sierra Leone Forecasts

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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 256 312 380 464 566 690 841 1025 1250 1523 1856

35% of cases are hospitalized

ReproducPve Number Community 1.20 Hospital 0.29 Funeral 0.15 Overall 1.63


Prevalence in SL

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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


Agent-‐based Model Progress

•  Synthe2c Informa2on Viewer for Ebola affected Countries – Assist in troubleshoo2ng simula2on results – Aid in calibra2on issues

•  Calibra2on – Rainy Season altera2on •  Considera2on of Mali and Senegal

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Synthe2c Informa2on Viewer

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Interface for exploring details of the popula2on and their ac2vi2es



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Zoom down to the household level to see rela2ve densi2es and selected details



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Zoom down to the individual and look at their ac2vity pa.ern


Calibra2on – Previous Steps

•  Disease Model representa2on •  Flowminder data used for travel •  Ini2alize simula2on in Lofa •  Road map with travel status used for prelim es2mate of travel altera2on

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Bomi Bong Gbarpolu Grand Bassa Grand Cape Mount Grand Gedeh Grand Kru Lofa Margibi Maryland Montserrado Nimba River Cess River Gee Sinoe Green 1 Bomi 0.55 0.5 1 0.5 0.425 0.425 0.1 1 0.425 1 0.55 0.1 0.425 0.3 Red 0.5

Bong 0.55 0.3 0.55 0.3 1 1 1 1 1 1 1 0.1 1 0.366666667 Black 0.1 Gbarpolu 0.5 0.3 0.5 0.5 0.425 0.425 0.1 0.5 0.425 0.5 0.3 0.1 0.425 0.3

Grand Bassa 1 0.55 0.5 0.5 0.3 0.3 0.4 1 0.3 1 0.55 0.1 0.3 0.3 Based on traveling from county capital to county capital

Grand Cape Mount 0.5 0.3 0.5 0.5 0.3 0.3 0.3 0.5 0.3 0.5 0.233333333 0.1 0.3 0.3 Based on 17SEPT2014 data Grand Gedeh 0.425 1 0.425 0.3 0.3 1 1 0.55 1 0.55 1 0.1 1 0.5

Grand Kru 0.425 1 0.425 0.3 0.3 1 1 0.533333333 1 0.533333333 1 0.1 1 0.5

Lofa 0.1 1 0.1 0.4 0.3 1 1 0.55 1 0.55 1 0.1 1 0.214285714

Margibi 1 1 0.5 1 0.5 0.55 0.533333333 0.55 0.533333333 1 1 0.1 0.533333333 0.3

Maryland 0.425 1 0.425 0.3 0.3 1 1 1 0.533333333 0.533333333 1 0.1 1 0.5

Montserrado 1 1 0.5 1 0.5 0.55 0.533333333 0.55 1 0.533333333 0.55 0.1 0.533333333 0.3

Nimba 0.55 1 0.3 0.55 0.233333333 1 1 1 1 1 0.55 0.1 1 0.233333333 River Cess 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

River Gee 0.425 1 0.425 0.3 0.3 1 1 1 0.533333333 1 0.533333333 1 0.1 0.5

Sinoe 0.3 0.366666667 0.3 0.3 0.3 0.5 0.5 0.2142857

14 0.3 0.5 0.3 0.233333333 0.1 0.5


Calibra2on – Spa2al Spread Simula2on

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Calibra2on – Spa2al Spread MoH

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Simula2on Comparison – spread from Lofa

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Cases per 100k popula2on

Mean simula2on Normal Travel Ministry of Health Data


Simula2on Comparison – Rainy Season Travel

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Cases per 100k popula2on

Mean simula2on Rainy Travel Ministry of Health Data



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Total Cases

Single Simula2on result – Normal Travel Ministry of Health Data



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Total Cases

Single Simula2on result – Rainy Travel Ministry of Health Data


Calibra2on Next Steps

•  Determine “right” 2me of rainy travel – Pursue more real-‐2me and comprehensive data

•  Combine all condi2ons and a.empt calibra2on – Lofa-‐based introduc2on – Lofa and other county temporal changes in txm – Regional travel – affected by rainy season

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Agent-‐based Next Steps

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

•  Study design / implementa2on under construc2on –  Vaccina2on campaign effec2veness

•  Framework under development –  Es2ma2on of surveillance coverage requirements

•  Simulate zoono2c and human introduc2on scenarios, look at “gold standard” transmission trees with varying level of completeness to represent different levels of surveillance

•  Address ques2on of needed resources for eventual final stages of “stamp out”

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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.

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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

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Parameters of two historical outbreaks

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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

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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

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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


Learning from Lofa

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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

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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

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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

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

modeling the ebola outbreak in west africa, november 18th 2014 update

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