Modeling the Ebola Outbreak in West Africa, 2014 Sept 16th Update Bryan Lewis PhD, MPH (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 Currently Used Data Guinea Liberia Nigeria Sierra Leone Total ● Cases 861 2407 22 1603 4893 Deaths 557 1137 8 524 2226 Data from WHO, MoH Liberia, and MoH Sierra Leone, available at 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 Liberia- Case Locations 3 Liberia – Health Care Workers 4 Liberia – Contact Tracing 5 Liberia – Community based cases 6 Sierra Leone – Case Locations 7 Sierra Leone – Case Finding 8 Sierra Leone – Case Finding 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) 9 Line Listing case_id exposure_date onset_date hospital_date 1 2013-12-02 http://www.nejm.org/doi/full/10.1056/NEJMoa1404505 2 http://www.nejm.org/doi/full/10.1056/NEJMoa1404506 3 2013-12-25 http://www.nejm.org/doi/full/10.1056/NEJMoa1404507 4 http://www.nejm.org/doi/full/10.1056/NEJMoa1404508 5 2014-01-29 http://www.nejm.org/doi/full/10.1056/NEJMoa1404509 6 2014-01-25 http://www.nejm.org/doi/full/10.1056/NEJMoa1404510 death_date recovery_date age country sub_location sub_sub_location legrand exposure Guinea Gueckedou Meliandou c zoonotic N F Guinea Gueckedou Meliandou c family F Guinea Gueckedou Meliandou c family elderly F Guinea Gueckedou c 2014-01-31 adult F Guinea Gueckedou 2014-02-02 adult F Guinea Gueckedou 2013-12-06 child 2013-12-13 adult 2013-12-27 child 2014-01-01 sex hcw source_id N 1 mother N 1 sister family Y 1 grandmother h hcw Y 1 nurse h hcw Y 1 midwife identifying_notes ource • 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 Line Listing - Epidemiology 11 Line Listing – Exposure Type 12 Line Listing – Transmission Trees 13 Twitter Tracking Most common images: Information about bushmeat, info about case locations, joke about soap cost, and dealing with Ebola patients, 14 Liberia Forecasts Forecast performance Reproductive Number Community 1.34 Hospital 0.35 Funeral 0.53 Overall 2.22 52% of Infected are hospitalized 8/13 – 8/19 8/20 – 8/26 8/27 – 9/02 9/3 – 9/9 9/10 – 9/16 9/179/23 175 353 321 468 544 -- Forecast 176 229 304 404 533 801 Actual 9/24 – 9/30 1064 15 Liberia Forecasts – Role of Prior Immunity 16 Sierra Leone Forecasts Forecast performance Reproductive Number Community 1.22 Hospital 0.23 Funeral 0.24 Overall 1.69 59% of cases are hospitalized 17 Prevalence of Cases 18 All Countries Forecasts 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 rI:0.85 rH:0.74 rF:0.31 Overal:1.90 19 Combined Forecasts 8/10 – 8/16 8/17 – 8/23 8/24 – 8/30 8/31– 9/6 9/8 – 9/13 9/149/20 9/21 – 9/27 9/28 – 10/4 Actual 231 442 559 783 681 -- -- -- Forecast 329 393 469 560 693 830 994 1191 20 Synthetic Sierra Leone Now integrated into the ISIS interface 21 ISIS - based Calibration 22 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 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 Supporting material describing model structure, and additional results APPENDIX 25 Further evidence of endemic Ebola • 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 26 Legrand et al. Model Description Susceptible Exposed not infectious Infectious Symptomatic Hospitalized Infectious Funeral 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. Infectious Removed Recovered and immune or dead and buried 27 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. 28 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 Parameters of two historical outbreaks 30 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 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 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 Liberia model params 34 Sierra Leone model params 35 All Countries model params 36 Long-term Operational Estimates Turn from 8-26 End Total Case from 8-26 Estimate 1 month 3 months 13,400 1 month 6 months 15,800 1 month 18 months 31,300 3 months 6 months 64,300 3 months 12 months 91,000 3 months 18 months 120,000 6 months 12 months 682,100 6 months 18 months 857,000 • Based on forced bend through extreme reduction in transmission coefficients, no evidence to support bends at these points – Long term projections are unstable 37