modeling the ebola outbreak in west africa, december 2nd 2014 update
TRANSCRIPT
DRAFT – Not for a.ribu2on or distribu2on
Modeling the Ebola Outbreak in West Africa, 2014
December 2nd 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-‐128
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.
Cases Deaths Guinea 2134 1260 Liberia 7244 3016 Sierra Leone 6911 1510 Total 16,311 5794
DRAFT – Not for a.ribu2on or distribu2on
Liberia Forecast
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
12/8 to
12/14
12/15 to
12/21
Reported 261 298 446 1604* 227 298 223 94 0 -‐-‐ -‐-‐ -‐-‐
Reported back log adjusted
657 549 691 490
Newer model
512 508 497 483 469 457 444 431 419 407 405 393
Reproduc2ve Number Community 0.3 Hospital 0.3 Funeral 0.3 Overall 0.9
DRAFT – Not for a.ribu2on or distribu2on
Sierra Leone Forecast
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
11/24 to
11/30
12/01 to
12/07
12/08 to
12/14
Reported 377 467 468 454 494 486 480 684 643 577 -‐-‐ -‐-‐
Forecast original 380 464 566 690 841 1025 1250 1523 1856
Forecast change txm 430 524 561 565 595 624 654 686 719
35% of cases are hospitalized
ReproducIve Number Community 1.10 Hospital 0.37 Funeral 0.15 Overall 1.63
DRAFT – Not for a.ribu2on or distribu2on
SL longer term forecast 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 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
Guinea Forecasts
40% of cases are hospitalized
ReproducIve Number Community 0.70 Hospital 0.13 Funeral 0.09 Overall 0.93
10/09 to
10/15
10/16 to
10/19
10/23to
10/29
10/30to
11/05
11/06 to
11/12
11/13 to
11/19
11/20 to
11/26
11/27 to
12/03
12/04 to
12/10
12/11 to
12/17
Reported 175 129 143 12 136 121 142 52 -‐-‐ -‐-‐
Forecast 118 118 115 112 109 106 103 100 97 94
DRAFT – Not for a.ribu2on or distribu2on
Guinea Prevalence Date People
needing care 9/1/14 77 9/8/14 87 9/15/14 100 9/22/14 114 9/29/14 130 10/5/14 140 10/12/14 140 10/19/14 137 10/26/14 133 11/2/14 129 11/9/14 126 11/16/14 122 11/23/14 118 11/30/14 115 12/7/14 112 12/14/14 108 12/21/14 105 12/28/14 102
DRAFT – Not for a.ribu2on or distribu2on
APPENDIX Suppor2ng material describing model structure, and addi2onal results
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.
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.
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
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
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
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