1. Modeling the Ebola Outbreak in West Africa, 2014 August 11 th Update Bryan Lewis PhD, MPH (blewis@vbi.vt.edu)blewis@vbi.vt.edu Caitlin Rivers MPH, Stephen Eubank PhD, Madhav Marathe 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 CasesDeaths Guinea495363 Liberia516282 Sierra Leone691286 Nigeria132 Total1779961 ● 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? Aug 8Jul 29

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 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

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 Liberia Disease Parameters for Model Fitting UgandaOutUganda_inDRCOutDRC_in beta_F0.8581.0930.0810.066 beta_H0.0910.1130.0030.002 beta_I0.1230.0840.2040.505 dx0.5850.6500.8670.670 gamma_I0.0500.1000.0790.100 gamma_d0.0840.1250.0500.104 gamma_f0.6650.5000.5120.500 gamma_h0.3350.2380.1530.200 Score62370NA103596NA

24. Sierra Leone Fitted Models Model Parameters No behavioral Changes included Sierra LeoneDisease Parameters for Model Fitting UgandaOutUganda_inDRCOutDRC_in beta_F1.7521.0930.0450.066 beta_H0.2600.1130.0010.002 beta_I0.0830.0840.2960.505 dx0.3230.6500.3000.670 gamma_I0.2470.1000.1490.100 gamma_d0.2110.1250.1590.104 gamma_f0.3300.5000.8140.500 gamma_h0.2470.2380.3330.200 Score140931NA114419NA

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