Modeling the Ebola Outbreak in West Africa, 2014 Sept 23 rd Update Bryan Lewis PhD, MPH...
41
Modeling the Ebola Outbreak in West Africa, 2014 Sept 23 rd Update Bryan Lewis PhD, MPH ([email protected]) Caitlin Rivers MPH, Eric Lofgren PhD, James Schlitt, Katie Dunphy, 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 Barrett PhD
Modeling the Ebola Outbreak in West Africa, 2014 Sept 23 rd Update Bryan Lewis PhD, MPH ([email protected])[email protected] Caitlin Rivers MPH, Eric Lofgren
Modeling the Ebola Outbreak in West Africa, 2014 Sept 23 rd
Update Bryan Lewis PhD, MPH ([email protected])[email protected]
Caitlin Rivers MPH, Eric Lofgren PhD, James Schlitt, Katie Dunphy,
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 Barrett PhD
Slide 2
Currently Used Data Data from WHO, MoH Liberia, and MoH Sierra
Leone, available at https://github.com/cmrivers/ebola
https://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 CasesDeaths Guinea861557 Liberia27121137 Nigeria228
Sierra Leone1603524 Total51982226
Slide 3
Epi Notes WHO reports results on case history analysis
providing clarity on some disease parameters NEJM NEJM CDC releases
their model with some dire forecasts MMWRMMWR Sierra Leone not
doing as well as they report More graves from Ebola patients than
reported cases NY TimesNY Times 3
Slide 4
Comparison of Parameters 4
Slide 5
Liberia- Case Locations 5
Slide 6
Liberia Contact Tracing 6
Slide 7
Contact Tracing Metrics 7
Slide 8
Sierra Leone Contact Tracing Efficiency 8
Slide 9
Sierra Leone Case Finding 9 Assuming all cases are followed to
the same degree, this what the observed Re would be based on cases
found from contacts (using time lagged 7,10,12 day reported cases
as denominator)
Slide 10
Twitter Tracking 10 Most common images: Solidarity with Ebola
affected countries, Jokes about bushmeat, Ebola risk, and names,
Positive health message
Learning from Lofa Lofa has experienced decreasing cases for
several weeks Exploring with contacts in MoH about whether these
are reporting artifact or reality and to understand what factors
are driving it The decrease starts at 0.13% of population infected
Montserrado is currently at 0.101%, model predicts this will occur
on 9/19 If we fit the decreased rate in Lofa what might
Monteserrado look like? Assuming equal decrease across all betas
until more info available 17
Slide 18
Learning from Lofa 18
Slide 19
Learning from Lofa 19
Slide 20
Hospital Beds Prelim analysis Proposed scenario of 70% in
hospital beds will tip epidemic Explore using Compartmental Model
Based on Liberia wide model Trigger change at a certain point in
time (ie instantaneously move up to 70%) Transmission in hospitals
also assumed to be 90% better than current fit 20
Slide 21
Hospital Beds Prelim analysis 21 Cases on Feb 1 Oct 1155k Nov
1226k Dec 1352k Jan 1521k No beds669k Impact in Liberia
Slide 22
Hospital Beds Discrete Rollout Using Stochastic model
Monteserrado model fit (very high transmission fit) 170 beds start
arriving every week from mid- October on These beds are assumed to
be 100% effective If beds are full, the current hospitals are
assumed to absorb No lower tier but better than current ECUs in
place 22
Slide 23
Hospital Beds Discrete Rollout 23
Slide 24
Synthetic Liberia 24 Now integrated into the CNIMS
interface
Slide 25
Agent-based Simulations Running simulations on two simulation
platforms EpiFast Fast, integrated with CNIMS interface, some
interventions and behaviors cant be represented EpiSimdemics Very
flexible, can represent nearly any conceivable behavior or
intervention, slower, and more cumbersome to execution 25
Slide 26
ABM of Monrovia 26
Slide 27
EpiSimdemics ABM running 27
Slide 28
Next steps Focus on agent-based model Incorporating regional
travel Re-calibrate with WHO based parameters Set up to incorporate
behaviors Address bed rollout in Stochastic Compartmental model
Sensitivity analysis to identify what capacities and assumed
reductions are necessary for turning the epidemic down. 28
Slide 29
APPENDIX Supporting material describing model structure, and
additional results 29
Slide 30
Further evidence of endemic Ebola 30 1985 manuscript finds ~13%
sero-prevalence of Ebola in remote Liberia Paired control study:
Half from epilepsy patients and half from healthy volunteers
Geographic and social group sub-analysis shows all affected
~equally
Slide 31
Legrand et al. Model Description Legrand, J, R F Grais, P Y
Boelle, A J Valleron, and A Flahault. Understanding the Dynamics of
Ebola Epidemics Epidemiology and Infection 135 (4). 2007. Cambridge
University Press: 61021. doi:10.1017/S0950268806007217. 31
Slide 32
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 Infection 135 (4). 2007. Cambridge University
Press: 61021. doi:10.1017/S0950268806007217. 32
Slide 33
Legrand et al. Approach Behavioral changes to reduce
transmissibilities at specified days Stochastic implementation fit
to two historical outbreaks Kikwit, DRC, 1995 Gulu, Uganda, 2000
Finds two different types of outbreaks Community vs. Funeral driven
outbreaks 33
Slide 34
Parameters of two historical outbreaks 34
Slide 35
NDSSL Extensions to Legrand Model Multiple stages of behavioral
change possible during this prolonged outbreak Optimization of fit
through automated method Experiment: Explore degree of fit using
the two different outbreak types for each country in current
outbreak 35
Slide 36
Optimized Fit Process Parameters to explored selected
Diag_rate, beta_I, beta_H, beta_F, gamma_I, gamma_D, gamma_F,
gamma_H Initial values based on two historical outbreak
Optimization routine Runs model with various permutations of
parameters Output compared to observed case count Algorithm chooses
combinations that minimize the difference between observed case
counts and model outputs, selects best one 36
Slide 37
Fitted Model Caveats Assumptions: Behavioral changes effect
each transmission route similarly Mixing occurs differently for
each of the three compartments but uniformly within These models
are likely overfitted Many combos of parameters will fit the same
curve Guided by knowledge of the outbreak and additional data
sources to keep parameters plausible Structure of the model is
supported 37
Slide 38
Liberia model params 38
Slide 39
Sierra Leone model params 39
Slide 40
All Countries model params 40
Slide 41
Long-term Operational Estimates Based on forced bend through
extreme reduction in transmission coefficients, no evidence to
support bends at these points Long term projections are unstable 41
Turn from 8-26 End from 8-26 Total Case Estimate 1 month3
months13,400 1 month6 months15,800 1 month18 months31,300 3 months6
months64,300 3 months12 months91,000 3 months18 months120,000 6
months12 months682,100 6 months18 months857,000