Modeling the Ebola Outbreak in West Africa, 2014

Modeling the Ebola Outbreak in West Africa, 2014Sept 30th 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 DataData from WHO, MoH Liberia, and MoH Sierra Leone, available at https://github.com/cmrivers/ebolaMoH and WHO have reasonable agreementSierra Leone case counts censored up to 4/30/14.Time series was filled in with missing dates, and case counts were interpolated.2CasesDeathsGuinea1074648Liberia33621830Nigeria228Sierra Leone2208605Total66663091

Figs and tables updated 9/82Epi NotesReports of efficacy of HIV drug lowering mortality CNNTwo other physicians infected with Ebola back in US, one at NIH enrolled in vax trial PoliticoSuspect cases continue to be identified in the US, currently a patient in Dallas (previous negatives from CA, NY, NM, FL) WaPoSierra Leones reporting still inconsistent Crawford Killian

3Liberia Case Locations4

Liberia Contact Tracing5

Liberia Forecasts68/18-8/248/25 8/31

9/01 9/07

9/08 9/14

9/15 9/219/22 9/299/30-10/6Actual431368421620558----Forecast31441755573898113041733

Forecast performanceReproductive NumberCommunity1.3Hospital0.4Funeral0.5Overall2.252% of Infected arehospitalized

Figs and tables updated 9/86Prevalence of Cases7WeekPeople in H+I9/28/2014122810/05/2014163110/12/2014216710/19/2014287810/26/2014382111/02/2014507111/16/20148911

Sierra Leone Forecasts8

Forecast performance41% of cases are hospitalized8/25 8/319/01 9/079/08 9/149/15 9/219/22-9/289/29 10/0610/06-10/12Actual196219194274332----Forecast267333413512635786974

Prevalence in SL9

WeekPeople in H+I9/28/201466810/05/201482810/12/2014102610/19/2014127110/26/2014157311/02/2014194711/16/20142978All Countries Forecasts10rI: 1.1rH:0.4rF:0.3Overall:1.7

Combined Forecasts118/18 8/248/25 8/319/1 9/79/8 9/149/15-9/219/22 9/289/29 10/510/6 -10/12Actual559783681959917------Forecast483578693830994119114261426

Figs and table updated 9/811ExperimentsHospital bed estimate calculationsReduction in time to hospitalizationImprovements in time from symptom onset to hospitalization

12Hospital Beds Prelim analysis13Cases on Feb 1Oct 1245kNov 1312kDec 1391kJan 1475kNo beds533kImpact in Liberia, beds only16% hospitalization ratio -> 70%Beta_H reduction by 90%

Hospital Beds Prelim analysis14Cases on Feb 1Oct 173kNov 1135kDec 1230kJan 1375kNo beds533kImpact in Liberia, beds andproper burial16% hospitalization ratio -> 70%Beta_H reduction by 90%Beta_F reduction by 90%

Hospital beds Prelim analysis15

Impact in Liberia, beds + proper burial + shortened time to hospitalization

Hospital beds Prelim analysis5 days3 days1 daysOct 152k25k10kNov 1108k65k31kDec 1206k152k92kJan 1358k318k250616Cumulative cases in Liberia on Feb 1 with reduced beta_H, reduced beta_F, and shortened time to hospitalizationOptimal center placementPreliminary optimization using road networks and population centers17

Agent-based Simulations ProgressRegional travel method, developedImplementation working this weekInterventional support designed forIncreasing hospitalization levelBetter burialDecreasing time to hospitalizationCapacity monitoring at ETU/ECU designedNeed some bounds on experimental design

18AppendixSupporting material describing model structure, and additional results19Legrand 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/S0950268806007217.20Compartmental ModelExtension 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.

21Legrand et al. ApproachBehavioral changes to reduce transmissibilities at specified daysStochastic implementation fit to two historical outbreaks Kikwit, DRC, 1995 Gulu, Uganda, 2000Finds two different types of outbreaksCommunity vs. Funeral driven outbreaks

22Parameters of two historical outbreaks

23NDSSL Extensions to Legrand ModelMultiple stages of behavioral change possible during this prolonged outbreakOptimization of fit through automated methodExperiment:Explore degree of fit using the two different outbreak types for each country in current outbreak

24Optimized Fit ProcessParameters to explored selectedDiag_rate, beta_I, beta_H, beta_F, gamma_I, gamma_D, gamma_F, gamma_HInitial values based on two historical outbreak

Optimization routineRuns model with various permutations of parametersOutput compared to observed case countAlgorithm chooses combinations that minimize the difference between observed case counts and model outputs, selects best one25Fitted Model CaveatsAssumptions:Behavioral changes effect each transmission route similarlyMixing occurs differently for each of the three compartments but uniformly withinThese models are likely overfittedMany combos of parameters will fit the same curveGuided by knowledge of the outbreak and additional data sources to keep parameters plausibleStructure of the model is supported26Liberia model params27

Sierra Leone model params28

All Countries model params29

Long-term Operational EstimatesBased on forced bend through extreme reduction in transmission coefficients, no evidence to support bends at these pointsLong term projections are unstable30

Turn from 8-26End from 8-26Total Case Estimate1 month3 months13,4001 month6 months15,8001 month18 months31,3003 months6 months64,3003 months12 months91,0003 months18 months120,0006 months12 months682,1006 months18 months857,000