Modeling the Ebola Outbreak in West Africa, 2014 Sept 23 rd 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

Currently Used Data ● Data from WHO, MoH Liberia, and MoH Sierra Leone, available at ● 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

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

Comparison of Parameters 4

Liberia- Case Locations 5

Liberia – Contact Tracing 6

Contact Tracing Metrics 7

Sierra Leone – Contact Tracing Efficiency 8

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)

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

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 Actual Forecast Forecast performance Reproductive Number Community1.5 Hospital0.1 Funeral0.4 Overall2.0 52% of Infected are hospitalized

Liberia Forecasts – Role of Prior Immunity 12

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

Prevalence of Cases 14

All Countries Forecasts 15 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

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 Actual Forecast Reproductive Number Community1.3 Hospital0.1 Funeral0.3 Overall1.7

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

Learning from Lofa 18

Learning from Lofa 19

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

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

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

Hospital Beds – Discrete Rollout 23

Synthetic Liberia 24 Now integrated into the CNIMS interface

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

ABM of Monrovia 26

EpiSimdemics ABM running 27

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

APPENDIX Supporting material describing model structure, and additional results 29

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

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 33

Parameters of two historical outbreaks 34

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

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

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

Liberia model params 38

Sierra Leone model params 39

All Countries model params 40

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