1. Modeling the Ebola Outbreak in West Africa, 2014 Sept 23 rd Update Bryan Lewis PhD, MPH (blewis@vbi.vt.edu)blewis@vbi.vt.edu Caitlin Rivers MPH, Eric Lofgren PhD, James Schlitt, Katie Dunphy, Henning Mortveit PhD, Dawen Xie MS, Samarth Swarup PhD, Hannah Chungbaek, Keith Bisset PhD, Maleq Khan PhD, Chris Kuhlman PhD, Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barrett PhD

2. Currently Used Data ● Data from WHO, MoH Liberia, and MoH Sierra Leone, available at https://github.com/cmrivers/ebola https://github.com/cmrivers/ebola ● MoH and WHO have reasonable agreement ● Sierra Leone case counts censored up to 4/30/14. ● Time series was filled in with missing dates, and case counts were interpolated. 2 CasesDeaths Guinea861557 Liberia27121137 Nigeria228 Sierra Leone1603524 Total51982226

3. Epi Notes WHO reports results on case history analysis providing clarity on some disease parameters NEJM NEJM CDC releases their model with some dire forecasts MMWRMMWR Sierra Leone not doing as well as they report – More graves from Ebola patients than reported cases – NY TimesNY Times 3

4. Comparison of Parameters 4

5. Liberia- Case Locations 5

6. Liberia – Contact Tracing 6

7. Contact Tracing Metrics 7

8. Sierra Leone – Contact Tracing Efficiency 8

9. Sierra Leone – Case Finding 9 Assuming all cases are followed to the same degree, this what the “observed” Re would be based on cases found from contacts (using time lagged 7,10,12 day reported cases as denominator)

10. Twitter Tracking 10 Most common images: Solidarity with Ebola affected countries, Jokes about bushmeat, Ebola risk, and names, Positive health message

11. Liberia Forecasts 11 8/13 – 8/19 8/20 – 8/26 8/27 – 9/02 9/3 – 9/9 9/10 – 9/16 9/17- 9/23 9/24 – 9/30 Actual175353321468544-- Forecast1762293044045338011105 Forecast performance Reproductive Number Community1.5 Hospital0.1 Funeral0.4 Overall2.0 52% of Infected are hospitalized

12. Liberia Forecasts – Role of Prior Immunity 12

13. Sierra Leone Forecasts 13 Forecast performance 41% of cases are hospitalized

14. Prevalence of Cases 14

15. All Countries Forecasts 15 rI:0.85 rH:0.74 rF:0.31 Overal:1.90 Model Parameters 'alpha':1/10 'beta_I':0.200121 'beta_H':0.029890 'beta_F':0.1 'gamma_h':0.330062 'gamma_d':0.043827 gamma_I':0.05 'gamma_f':0.25 'delta_1':.55 'delta_2':.55 'dx':0.6

16. Combined Forecasts 16 8/10 – 8/16 8/17 – 8/23 8/24 – 8/30 8/31– 9/6 9/8 – 9/13 9/14- 9/20 9/21 – 9/27 9/28 – 10/4 Actual231442559783681-- Forecast32939346956069383010071213 Reproductive Number Community1.3 Hospital0.1 Funeral0.3 Overall1.7

17. Learning from Lofa Lofa has experienced decreasing cases for several weeks – Exploring with contacts in MoH about whether these are reporting artifact or reality and to understand what factors are driving it The decrease starts at 0.13% of population infected – Montserrado is currently at 0.101%, model predicts this will occur on 9/19 If we fit the decreased rate in Lofa what might Monteserrado look like? – Assuming equal decrease across all betas until more info available 17

18. Learning from Lofa 18

19. Learning from Lofa 19

20. Hospital Beds – Prelim analysis Proposed scenario of 70% in hospital beds will tip epidemic Explore using Compartmental Model – Based on Liberia wide model – Trigger change at a certain point in time (ie instantaneously move up to 70%) – Transmission in hospitals also assumed to be 90% better than current fit 20

21. Hospital Beds – Prelim analysis 21 Cases on Feb 1 Oct 1155k Nov 1226k Dec 1352k Jan 1521k No beds669k Impact in Liberia

22. Hospital Beds – Discrete Rollout Using Stochastic model – Monteserrado model fit (very high transmission fit) – 170 beds start arriving every week from mid- October on – These beds are assumed to be 100% effective – If beds are full, the current “hospitals” are assumed to absorb – No lower tier but better than current ECUs in place 22

23. Hospital Beds – Discrete Rollout 23

24. Synthetic Liberia 24 Now integrated into the CNIMS interface

25. Agent-based Simulations Running simulations on two simulation platforms – EpiFast – Fast, integrated with CNIMS interface, some interventions and behaviors can’t be represented – EpiSimdemics – Very flexible, can represent nearly any conceivable behavior or intervention, slower, and more cumbersome to execution 25

26. ABM of Monrovia 26

27. EpiSimdemics ABM running 27

28. Next steps Focus on agent-based model – Incorporating regional travel – Re-calibrate with WHO based parameters – Set up to incorporate behaviors Address bed rollout in Stochastic Compartmental model – Sensitivity analysis to identify what capacities and assumed reductions are necessary for turning the epidemic down. 28

29. APPENDIX Supporting material describing model structure, and additional results 29

30. Further evidence of endemic Ebola 30 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

31. 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. 31

32. 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. 32

33. 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 33

34. Parameters of two historical outbreaks 34

35. 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 35

36. 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 36

37. 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 37

38. Liberia model params 38

39. Sierra Leone model params 39

40. All Countries model params 40

41. 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 41 Turn from 8-26 End from 8-26 Total Case Estimate 1 month3 months13,400 1 month6 months15,800 1 month18 months31,300 3 months6 months64,300 3 months12 months91,000 3 months18 months120,000 6 months12 months682,100 6 months18 months857,000