modeling the ebola outbreak in west africa, 2014 august 11 th update bryan lewis phd, mph...
TRANSCRIPT
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Modeling the Ebola Outbreak in West Africa, 2014
August 11th UpdateBryan Lewis PhD, MPH ([email protected])
Caitlin Rivers MPH, Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barrett PhD
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Goals
• Estimate future cases in Africa• Offer any guidance on potential for
transmission in the United States• Explore impact of various countermeasures
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Data Sources
• Using case counts from WHO for Model Fitting– Lots of variability from different sources, generally similar– Challenging to estimate what proportion of infections are
captured
• Liberia’s Ministry of Health for Model Selection and geographic resolution
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Currently Used WHO DataCases Deaths
Guinea 495 363
Liberia 516 282
Sierra Leone 691 286
Nigeria 13 2
Total 1779 961
● Data reported by WHO on Aug 8 for cases as of Aug 6
● 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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Measure of Awareness?
Aug 8Jul 29
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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 Infection 135 (4). 2007. Cambridge University Press: 610–21. doi:10.1017/S0950268806007217.
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Legrand et al. Model Description
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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
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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
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Liberia Fitted Models
Assuming no impact from ongoing responsesand DRC parameter fit is correct:
142 cases in next week182 cases in the following week
Assuming no impact from ongoing responsesand Uganda parameter fit is correct:
178 cases in next week235 cases in the following week
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Liberia Fitted ModelsSources of Infections
Currently 14% of Liberian Infections among HCWSupports use of “Uganda” parameter set
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Liberia Forecasts over time
1. Model trained on Liberian data, using “Uganda” parameters up to specified date
2. Model projected past “trained to” date
3. Complete case count data provided for reference
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Sierra Leone Fitted Models
Assuming no impact from ongoing responsesand DRC parameter fit is correct:
208 cases in next week267 cases in the following week
Assuming no impact from ongoing responsesand Uganda parameter fit is correct:
211 cases in next week273 cases in the following week
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Sierra Leone Forecasts over time
Model trained on Sierra Leone data up to specified date, projected into future, Complete case count data provided for reference
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Explore Intervention Requirements
Vaccination of large swaths of population required to reduce txm, unless a targeted strategy is used
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Explore Intervention Requirements
This does not capture reduction in deaths, but shows nominal interruption of transmission
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Notional US estimates
• Under assumption that Ebola case, arrives and doesn’t seek care and avoids detection throughout illness
• CNIMS based simulations– Agent-based models of populations with realistic social
networks, built up from high resolution census, activity, and location data
• Assume:– Reduced transmission Ebola 70% less likely to infect in
home and 95% less likely to infect outside of home than respiratory illness
– Transmission calibrated to R0 of 3.5 if transmission is like flu
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Notional US estimates Approach
• Get disease parameters from fitted model in West Africa
• Put into CNIMS platform– ISIS simulation GUI– Modify to represent US
• Example Experiment:– 100 replicates – One case introduction into Washington DC– Simulate for 3 weeks
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Notional US estimates Example
100 replicatesMean of 1.8 casesMax of 6 casesMajority only one initial case
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Conclusions
• Still need more information (though more is becoming available) to remove uncertainty in estimates
• From available data and in the absence of significant mitigation outbreak in Africa looks to continue to produce significant numbers of cases in the coming weeks
• Under current assumptions, Ebola transmission hard to interrupt in Africa with “therapeutics” alone
• Expert opinion and preliminary simulations support limited spread in US context
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Next Steps
• Gather further data from news media and reports to support model parameter selection
• Build patch model framework to incorporate more geographic location information
• Build more detailed population of area to support agent based simulations
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ADDITIONAL SLIDES FOR MORE DETAILS
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Liberia Fitted Models
Model Parameters
No behavioral Changes included
Liberia Disease Parameters for Model Fitting UgandaOut Uganda_in DRCOut DRC_in
beta_F 0.858 1.093 0.081 0.066beta_H 0.091 0.113 0.003 0.002beta_I 0.123 0.084 0.204 0.505dx 0.585 0.650 0.867 0.670gamma_I 0.050 0.100 0.079 0.100gamma_d 0.084 0.125 0.050 0.104gamma_f 0.665 0.500 0.512 0.500gamma_h 0.335 0.238 0.153 0.200Score 62370 NA 103596 NA
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Sierra Leone Fitted Models
Model Parameters
No behavioral Changes included
Sierra LeoneDisease Parameters for Model Fitting UgandaOut Uganda_in DRCOut DRC_in
beta_F 1.752 1.093 0.045 0.066beta_H 0.260 0.113 0.001 0.002beta_I 0.083 0.084 0.296 0.505dx 0.323 0.650 0.300 0.670gamma_I 0.247 0.100 0.149 0.100gamma_d 0.211 0.125 0.159 0.104gamma_f 0.330 0.500 0.814 0.500gamma_h 0.247 0.238 0.333 0.200Score 140931 NA 114419 NA
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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
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Parameters of two historical outbreaks
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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