DRAFT – Not for a.ribu2on or distribu2on Modeling the Ebola Outbreak in West Africa, 2014 Nov 4 th 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 #14113
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Modeling the Ebola Outbreak in West Africa, November 4th 2014 update
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
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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
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 1906 997 Liberia 6454 2705 Sierra Leone 5235 1500 Total 13,617 5210
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.
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US overall experience to date 1 transmission for every 1716 exposure hours (71.5 days)
US Healthcare System
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Agent-‐based Model Progress
• Calibra2on progress – Spa2al spread guided by seeding
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Calibra2on – Spa2al Spread
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Simula2on Comparison
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Cases per 100k popula2on
Mean simula2on results Ministry of Health Data
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Simula2on Comparison
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Total Cases
Single Simula2on result Ministry of Health Data
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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
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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