DRAFT – Not for a.ribu2on or distribu2on DRAFT – Not for a.ribu2on or distribu2on Modeling the Ebola Outbreak in West Africa, 2014 December 22 nd 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 #14131
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Modeling the Ebola Outbreak in West Africa, December 22nd 2014 update
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DRAFT – Not for a.ribu2on or distribu2on DRAFT – Not for a.ribu2on or distribu2on
Modeling the Ebola Outbreak in West Africa, 2014
December 22nd 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-‐131
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
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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.
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Cases Deaths Guinea 2,416 1,525 Liberia 7,830 3,290 Sierra Leone 8,759 2085 Total 19,005 6,900
• Bias towards household members – 70% less likely to transmit outside the household
• Hospital Isola2on – 57% isolated with 82% efficacy
• Proper Burial – 58% buried with 77% reduc2on
• Behavioral Change – mid Oct – 16% reduc2on in effec2ve contacts
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Agent-‐Based Model Next Steps
• Targeted Vaccines – Debug
• Sierra Leone upda2ng – Na2onal spread calibrated – Work on Geographic spread – Run similar prelim study
• Genera2on of Synthe2c disease spread as public resource for designing vax trials and campaigns
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DRAFT – Not for a.ribu2on or distribu2on
FORECAST COMPARISON TOOL EpiViewer
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DRAFT – Not for a.ribu2on or distribu2on
EpiViewer
• Portal for exploring, sharing, comparing, and dissemina2ng forecasts of infec2ous diseases
• Currently set up for Ebola Forecasts • Intended as a community tool – Hosted on public facing URL (once finalized) – Offers simple controls for exploring the data – Allows independent uploading by 3rd par2es to facilitate sharing, as well as consolidated downloading of data
APPENDIX Suppor2ng material describing model structure, and addi2onal results
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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.
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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.
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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
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DRAFT – Not for a.ribu2on or distribu2on
Parameters of two historical outbreaks
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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
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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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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