1. Modeling the Ebola Outbreak in West Africa, 2014 Sept 2 nd Update Bryan Lewis PhD, MPH (blewis@vbi.vt.edu)blewis@vbi.vt.edu Caitlin Rivers MPH, Eric Lofgren PhD, James Schlitt, Katie Dunphy, Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barrett PhD

2. Currently Used WHO Data CasesDeaths Guinea648430 Liberia1378694 Sierra Leone1026422 Nigeria176 Total30691563 ● Data reported by WHO on Aug 29 for cases as of Aug 26 ● Sierra Leone case counts censored up to 4/30/14. ● Time series was filled in with missing dates, and case counts were interpolated. 2

3. Epi Notes Case identified in Senegal – Guinean student, sought care in Dakar, identified and quarantined though did not report exposure to Ebola, thus HCWs were exposed. BBCBBC Liberian HCWs survival credited to Zmapp – Dr. Senga Omeonga and physician assistant Kynda Kobbah were discharged from a Liberian treatment center on Saturday after recovering from the virus, according to the World Health Organization. CNNCNN 3

4. Epi Notes Guinea riot in Nzerekore (2 nd city) on Aug 29 – Market area “disinfected,” angry residents attack HCW and hospital, “Ebola is a lie” BBCBBC India quarantines 6 “high-risk” Ebola suspects on Monday in New Delhi – Among 181 passengers who arrived in India from the affected western African countries HealthMapHealthMap 4

5. Further evidence of endemic Ebola 5 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

6. Twitter Tracking 6 Most common images: Risk map, lab work (britain), joke cartoon, EBV rally

7. Liberia Forecasts 7

8. rI: 0.95 rH: 0.65 rF: 0.61 R0 total: 2.22 8 8/6 – 8/12 8/13 – 8/19 8/20 – 8/26 8/27 – 9/02 9/3 – 9/9 9/10 – 9/16 Actual163232296 -- Forecast133176234310410543 Model Parameters 'alpha':1/12, 'beta_I':0.17950, 'beta_H':0.062036, 'beta_F':0.489256, 'gamma_h':0.308899, 'gamma_d':0.075121, 'gamma_I':0.050000, 'gamma_f':0.496443, 'delta_1':.5, 'delta_2':.5, 'dx':0.510845

9. Liberia Vaccinations 9 20% of population Vaccinated on Nov 1 st and Jan 1 st Additional Infections Prevented (by April 2015): Nov 1 st - ~275k Jan 1 st - ~225k

10. New model for Liberia Due to continued underestimation, have refit model – Small increases in betas change the fit compared to “stable” fit of last 3 weeks – May shift to this model for future forecasts 10

11. Sierra Leone Epi Details asdfsdf 11 By Sierra Leone MoH has 1077 cases (vs. 1026 as reported by WHO)

12. Sierra Leone Forecasts 12

13. Sierra Leone Forecasts rI:0.85 rH:0.74 rF:0.31 R0 total: 1.90 13 8/6 – 8/12 8/13 – 8/19 8/20 – 8/26 8/27 – 9/02 9/3 – 9/9 9/10 – 9/16 Actual14393100-- Forecast135168209260324405 Model Parameters 'alpha':1/10 'beta_I':0.164121 'beta_H':0.048990 'beta_F':.16 'gamma_h':0.296 'gamma_d':0.044827 'gamma_I':0.055 'gamma_f':0.25 'delta_1':.55 delta_2':.55 'dx':0.58

14. Sierra Leone Vaccinations 14 100k on Nov 1 st 200k on Jan 1 st Additional Infections prevented (by April 2015) Nov 1 st - ~6k Jan 1 st - ~7.5k

15. All Countries Forecasts 15 rI:0.85 rH:0.74 rF:0.31 Overal:1.90

16. All Countries Vaccinations 16 100k on Nov 1 st 200k on Jan 1 st Additional Infections prevented (by April 2015) Nov 1 st - ~3.2k Jan 1 st - ~4.0k Need more than just vaccine to interupt transmission

17. Extracting the Guinea experience Result: Not enough information in early slight decrease to harvest meaningful impacts. – Model won’t fit well Conclusion: Likely need to wait another week or so to assess impacts of recent new push on interventions to incorporate their impact 17

18. 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 18 Turn from 8-26 End from 8-26 Total Case Estimate 1 month6 months15,800 1 month18 months31,300 3 months6 months64,300 3 months18 months120,000 6 months9 months599,000 6 months18 months857,000

19. Next Steps Detailed HCW infection analysis underway – Looking at exposure and infections in Liberia to assess the attrition rates of HCW under current conditions Initial version of Sierra Leone constructed – Initial look at sublocation modeling required a re- adjustment – Should start simulations this week Build similar versions for other affected countries 19

20. Next steps Publications – One submitted, another in the works – 2 quick communications in prep Problems appropriate for agent-based approach – Logistical questions surrounding delivery and use of medical supplies – Effects of limited HCW both direct and indirect – Synthetic outbreaks to compare to what we’ve observed of this one, to estimate true size 20

21. APPENDIX Supporting material describing model structure, and previous results 21

22. 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: 610–21. doi:10.1017/S0950268806007217. 22

23. 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. 23

24. 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 24

25. Parameters of two historical outbreaks 25

26. 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 26

27. 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 27

28. 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 28

29. 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 29

30. Notional US estimates Assumptions 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: – Transmission calibrated to R0 of 3.5 if transmission is like flu – Reduced transmission Ebola 70% less likely to infect in home and 95% less likely to infect outside of home than respiratory illness 30

31. Notional US estimates Example 100 replicates Mean of 1.8 cases Max of 6 cases Majority only one initial case 31