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Modeling the Ebola Outbreak in West Africa, 2014 Sept 30 th Update Bryan Lewis PhD, MPH Caitlin Rivers MPH, Eric Lofgren.

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Presentation on theme: "Modeling the Ebola Outbreak in West Africa, 2014 Sept 30 th Update Bryan Lewis PhD, MPH Caitlin Rivers MPH, Eric Lofgren."— Presentation transcript:

1 Modeling the Ebola Outbreak in West Africa, 2014 Sept 30 th Update Bryan Lewis PhD, MPH 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 Guinea Liberia Nigeria228 Sierra Leone Total

3 Epi Notes Reports of efficacy of HIV drug “” lowering mortality CNNCNN Two other physicians infected with Ebola back in US, one at NIH enrolled in vax trial PoliticoPolitico Suspect cases continue to be identified in the US, currently a patient in Dallas (previous negatives from CA, NY, NM, FL) WaPoWaPo Sierra Leone’s reporting still inconsistent Crawford Killian Crawford Killian 3

4 Liberia – Case Locations 4

5 Liberia – Contact Tracing 5

6 Liberia Forecasts 6 8/18- 8/24 8/25 – 8/31 9/01– 9/07 9/08 – 9/14 9/15 – 9/21 9/22 – 9/29 9/30- 10/6 Actual Forecast Forecast performance Reproductive Number Community1.3 Hospital0.4 Funeral0.5 Overall2.2 52% of Infected are hospitalized

7 Prevalence of Cases 7 WeekPeople in H+I 9/28/ /05/ /12/ /19/ /26/ /02/ /16/

8 Sierra Leone Forecasts 8 Forecast performance 41% of cases are hospitalized 8/25 – 8/31 9/01– 9/07 9/08 – 9/14 9/15 – 9/21 9/22- 9/28 9/29 – 10/06 10/06- 10/12 Actual Forecast

9 Prevalence in SL 9 WeekPeople in H+I 9/28/ /05/ /12/ /19/ /26/ /02/ /16/

10 All Countries Forecasts 10 rI: 1.1 rH:0.4 rF:0.3 Overall:1.7

11 Combined Forecasts 11 8/18 – 8/24 8/25 – 8/31 9/1– 9/7 9/8 – 9/14 9/15- 9/21 9/22 – 9/28 9/29 – 10/5 10/6 - 10/12 Actual Forecast

12 Experiments Hospital bed estimate calculations Reduction in time to hospitalization Improvements in time from symptom onset to hospitalization 12

13 Hospital Beds – Prelim analysis 13 Cases on Feb 1 Oct 1245k Nov 1312k Dec 1391k Jan 1475k No beds533k Impact in Liberia, beds only 16% hospitalization ratio -> 70% Beta_H reduction by 90%

14 Hospital Beds – Prelim analysis 14 Cases on Feb 1 Oct 173k Nov 1135k Dec 1230k Jan 1375k No beds533k Impact in Liberia, beds and proper burial 16% hospitalization ratio -> 70% Beta_H reduction by 90% Beta_F reduction by 90%

15 Hospital beds – Prelim analysis 15 Impact in Liberia, beds + proper burial + shortened time to hospitalization

16 Hospital beds – Prelim analysis 5 days3 days1 days Oct 152k25k10k Nov 1108k65k31k Dec 1206k152k92k Jan 1358k318k Cumulative cases in Liberia on Feb 1 with reduced beta_H, reduced beta_F, and shortened time to hospitalization

17 Optimal center placement Preliminary optimization using road networks and population centers 17

18 Agent-based Simulations Progress Regional travel method, developed – Implementation working this week Interventional support designed for – Increasing hospitalization level – Better burial – Decreasing time to hospitalization Capacity monitoring at ETU/ECU designed – Need some bounds on experimental design 18

19 APPENDIX Supporting material describing model structure, and additional results 19

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

21 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

22 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 22

23 Parameters of two historical outbreaks 23

24 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 24

25 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 25

26 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 26

27 Liberia model params 27

28 Sierra Leone model params 28

29 All Countries model params 29

30 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 30 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


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