Modeling the Ebola Outbreak in West Africa, 2014 August 11 th Update Bryan Lewis PhD, MPH Caitlin Rivers MPH, Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barrett PhD

Goals Estimate future cases in Africa Offer any guidance on potential for transmission in the United States Explore impact of various countermeasures

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

Currently Used WHO Data CasesDeaths Guinea Liberia Sierra Leone Nigeria132 Total ● 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.

Measure of Awareness? Aug 8Jul 29

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) Cambridge University Press: 610–21. doi: /S

Legrand et al. Model Description

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

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

Liberia Fitted Models Assuming no impact from ongoing responses and DRC parameter fit is correct: 142 cases in next week 182 cases in the following week Assuming no impact from ongoing responses and Uganda parameter fit is correct: 178 cases in next week 235 cases in the following week

Liberia Fitted Models Sources of Infections Currently 14% of Liberian Infections among HCW Supports use of “Uganda” parameter set

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

Sierra Leone Fitted Models Assuming no impact from ongoing responses and DRC parameter fit is correct: 208 cases in next week 267 cases in the following week Assuming no impact from ongoing responses and Uganda parameter fit is correct: 211 cases in next week 273 cases in the following week

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

Explore Intervention Requirements Vaccination of large swaths of population required to reduce txm, unless a targeted strategy is used

Explore Intervention Requirements This does not capture reduction in deaths, but shows nominal interruption of transmission

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

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

Notional US estimates Example 100 replicates Mean of 1.8 cases Max of 6 cases Majority only one initial case

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

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

ADDITIONAL SLIDES FOR MORE DETAILS

Liberia Fitted Models Model Parameters No behavioral Changes included Liberia Disease Parameters for Model Fitting UgandaOutUganda_inDRCOutDRC_in beta_F beta_H beta_I dx gamma_I gamma_d gamma_f gamma_h Score62370NA103596NA

Sierra Leone Fitted Models Model Parameters No behavioral Changes included Sierra LeoneDisease Parameters for Model Fitting UgandaOutUganda_inDRCOutDRC_in beta_F beta_H beta_I dx gamma_I gamma_d gamma_f gamma_h Score140931NA114419NA

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

Parameters of two historical outbreaks

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