Modeling the Ebola Outbreak in West Africa, 2014

1. Modeling the Ebola Outbreak in West Africa, 2014 August 11thUpdate Bryan Lewis PhD, MPH (blewis@vbi.vt.edu) Caitlin Rivers MPH, Stephen Eubank PhD, MadhavMarathe PhD, and Chris Barrett PhD

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

3. 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

4. Currently Used WHO Data • 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.

5. Measure of Awareness? Jul 29 Aug 8

6. 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.

7. Legrand et al. Model Description

8. 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

9. 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

10. 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

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

12. Liberia Forecasts over time Model trained on Liberian data, using “Uganda” parameters up to specified date Model projected past “trained to” date Complete case count data provided for reference

13. 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

14. 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

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

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

17. 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

18. 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

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

20. 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

21. 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

22. Additional Slides for more Details

23. Liberia Fitted Models Model Parameters No behavioral Changes included

24. Sierra Leone Fitted Models Model Parameters No behavioral Changes included

25. 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

26. Parameters of two historical outbreaks

27. 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