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Modeling the Ebola Outbreak in West Africa, 2014 Sept 16 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 16 th Update Bryan Lewis PhD, MPH Caitlin Rivers MPH, Eric Lofgren."— Presentation transcript:

1 Modeling the Ebola Outbreak in West Africa, 2014 Sept 16 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 ● 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 Liberia- Case Locations 3

4 Liberia – Health Care Workers 4

5 Liberia – Contact Tracing 5

6 Liberia – Community based cases 6

7 Sierra Leone – Case Locations 7

8 Sierra Leone – Case Finding 8

9 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 Line Listing Gathered 50 case descriptions from media reports Tried to piece together all info we’d like access to from “comprehensive source” case_id,exposure_date,onset_date,hospital_date,death_date,recovery_date,age,sex,country,sub_location,sub_sub_locati on,legrand,exposure,hcw,source_id,identifying_notes,source 10 case_idexposure_dateonset_datehospital_datedeath_daterecovery_dateagesexcountrysub_locationsub_sub_locationlegrandexposurehcwsource_ididentifying_notesource childGuineaGueckedouMeliandouczoonoticN adultFGuineaGueckedouMeliandoucfamilyN1mother childFGuineaGueckedouMeliandoucfamilyN1sister elderlyFGuineaGueckedoucfamilyY1grandmother adultFGuineaGueckedouhhcwY1nurse adultFGuineaGueckedouhhcwY1midwife

11 Line Listing - Epidemiology 11

12 Line Listing – Exposure Type 12

13 Line Listing – Transmission Trees 13

14 Twitter Tracking 14 Most common images: Information about bushmeat, info about case locations, joke about soap cost, and dealing with Ebola patients,

15 Liberia Forecasts 15 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 Actual Forecast Forecast performance Reproductive Number Community1.34 Hospital0.35 Funeral0.53 Overall % of Infected are hospitalized

16 Liberia Forecasts – Role of Prior Immunity 16

17 Sierra Leone Forecasts Reproductive Number Community1.22 Hospital0.23 Funeral0.24 Overall Forecast performance 59% of cases are hospitalized

18 Prevalence of Cases 18

19 All Countries Forecasts 19 rI:0.85 rH:0.74 rF:0.31 Overal:1.90 Model Parameters 'alpha':1/10 'beta_I': 'beta_H': 'beta_F':0.1 'gamma_h': 'gamma_d': gamma_I':0.05 'gamma_f':0.25 'delta_1':.55 'delta_2':.55 'dx':0.6

20 Combined Forecasts 20 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 Actual Forecast

21 Synthetic Sierra Leone 21 Now integrated into the ISIS interface

22 ISIS - based Calibration 22

23 Next Steps - Compartmental Interventions under way – More hospital beds in urban areas – More “home-care” kits in rural areas – Arrival of therapeutics Inform the agent-based model – Geographic disaggregation – Parameter estimation – Intervention comparison 23

24 Next Steps – Agent-based Implement new disease mapping – Has been Add regional mobility ABM stochastic space larger than compartmental, how to accommodate? Integrating data to assist in logistical questions – Locations of ETCs, lab facilities from OCHA – Road network – Capacities of existing support operations 24

25 APPENDIX Supporting material describing model structure, and additional results 25

26 Further evidence of endemic Ebola 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

27 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

28 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

29 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 29

30 Parameters of two historical outbreaks 30

31 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 31

32 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 32

33 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 33

34 Liberia model params 34

35 Sierra Leone model params 35

36 All Countries model params 36

37 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 37 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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