Modeling the Ebola Outbreak in West Africa, 2014 Sept 5 th Update Bryan Lewis PhD, MPH Caitlin Rivers MPH, Eric Lofgren PhD, James Schlitt, Katie Dunphy, Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barrett PhD

Currently Used Data CasesDeaths Guinea Liberia Sierra Leone Nigeria217 Total ● Data from WHO, MoH Liberia, and MoH Sierra Leone, available here: ● ● 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 Forecasts rI: 0.95 rH: 0.65 rF: 0.61 R0 total: /6 – 8/12 8/13 – 8/19 8/20 – 8/26 8/27 – 9/02 9/3 – 9/9 9/10 – 9/16 Actual Forecast Model Parameters 'alpha':1/12, 'beta_I': , 'beta_H': , 'beta_F': , 'gamma_h': , 'gamma_d': , 'gamma_I': , 'gamma_f': , 'delta_1':.5, 'delta_2':.5, 'dx': Forecast performance

Forecasting Resource Demand Accounting for prevalent cases in the model – Can include their modeled state: community, hospital, or burial Help with logisitical planning 4

Exhausting Health Care System Model adjusted to have limited capacity “better” health compartment (sized: 300, 500, 1000, 2000 beds) added to existing “degraded” health compartment (previous fit) Those in new health compartment assumed to be – Well isolated and the dead are buried properly (ie once in the health system, very limited transmission to community 90% less than original fit) More beds have a measurable impact in total cases at 2 months, but does not halt transmission alone 5

Next Steps Agent-based modeling: – Initial version of Sierra Leone constructed – Need more work on mixing estimates – Initial look at sublocation modeling required a re- adjustment – Gathering data to assist in logistical questions Further refinement of compartmental model to look at health-care system questions – Impact of increased / decreased effectiveness 6

APPENDIX Supporting material describing model structure, and additional results 7

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 8

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 9

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

Twitter Tracking 11 Most common images: Risk map, lab work (britain), joke cartoon, EBV rally

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

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

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 14

Parameters of two historical outbreaks 15

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 16

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 17

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 18

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

All Countries Forecasts 20 rI:0.85 rH:0.74 rF:0.31 Overal:1.90

Exhausting Health Care System 21 Model adjusted to have limited capacity “better” health compartment (sized: 300, 500, 1000, 2000 beds) added to existing “degraded” health compartment (previous fit) Those in new health compartment assumed to be – Well isolated and the dead are buried properly (ie once in the health system, very limited transmission to community 90% less than original fit) More beds have a measurable impact in total cases at 2 months, but does not halt transmission alone

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 22 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