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Modeling the Ebola Outbreak in West Africa, 2014 Sept 23 rd Update Bryan Lewis PhD, MPH (blewis@vbi.vt.edu)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

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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 Guinea861557 Liberia27121137 Nigeria228 Sierra Leone1603524 Total51982226

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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

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Comparison of Parameters 4

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Liberia- Case Locations 5

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Liberia Contact Tracing 6

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Contact Tracing Metrics 7

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Sierra Leone Contact Tracing Efficiency 8

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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)

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

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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 Actual175353321468544-- Forecast1762293044045338011105 Forecast performance Reproductive Number Community1.5 Hospital0.1 Funeral0.4 Overall2.0 52% of Infected are hospitalized

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Liberia Forecasts Role of Prior Immunity 12

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

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Prevalence of Cases 14

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All Countries Forecasts 15 rI:0.85 rH:0.74 rF:0.31 Overal:1.90 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

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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 Actual231442559783681-- Forecast32939346956069383010071213 Reproductive Number Community1.3 Hospital0.1 Funeral0.3 Overall1.7

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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

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Learning from Lofa 18

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Learning from Lofa 19

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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

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

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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

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Hospital Beds Discrete Rollout 23

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Synthetic Liberia 24 Now integrated into the CNIMS interface

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Agent-based Simulations Running simulations on two simulation platforms EpiFast Fast, integrated with CNIMS interface, some interventions and behaviors cant be represented EpiSimdemics Very flexible, can represent nearly any conceivable behavior or intervention, slower, and more cumbersome to execution 25

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ABM of Monrovia 26

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EpiSimdemics ABM running 27

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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

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APPENDIX Supporting material describing model structure, and additional results 29

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Further evidence of endemic Ebola 30 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

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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). 2007. Cambridge University Press: 61021. doi:10.1017/S0950268806007217. 31

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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: 61021. doi:10.1017/S0950268806007217. 32

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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

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Parameters of two historical outbreaks 34

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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

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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

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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

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Liberia model params 38

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Sierra Leone model params 39

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All Countries model params 40

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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