This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Modeling the Ebola Outbreak in West Africa, 2014
Sept 16th Update
Bryan Lewis PhD, MPH ([email protected])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
Sierra Leone – Case FindingAssuming 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)
10
Line Listing
• 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_location,legrand,exposure,hcw,source_id,identifying_notes,source
case_id exposure_date onset_date hospital_date death_date recovery_date age sex countrysub_location sub_sub_location legrand exposure hcw source_id identifying_notes ource
1 2013-12-02 2013-12-06 child GuineaGueckedou Meliandou c zoonotic N http://www.nejm.org/doi/full/10.1056/NEJMoa1404505
2 2013-12-13 adult F GuineaGueckedou Meliandou c family N 1 mother http://www.nejm.org/doi/full/10.1056/NEJMoa1404506
3 2013-12-25 2013-12-27 child F GuineaGueckedou Meliandou c family N 1 sister http://www.nejm.org/doi/full/10.1056/NEJMoa1404507
4 2014-01-01 elderly F GuineaGueckedou c family Y 1 grandmother http://www.nejm.org/doi/full/10.1056/NEJMoa1404508
5 2014-01-29 2014-01-31 adult F GuineaGueckedou h hcw Y 1 nurse http://www.nejm.org/doi/full/10.1056/NEJMoa1404509
6 2014-01-25 2014-02-02 adult F GuineaGueckedou h hcw Y 1 midwife http://www.nejm.org/doi/full/10.1056/NEJMoa1404510
11
Line Listing - Epidemiology
12
Line Listing – Exposure Type
13
Line Listing – Transmission Trees
14
Twitter TrackingMost common images:
Information about bushmeat, info about case locations, joke about soap cost, and dealing with Ebola patients,
Model Parameters'alpha':1/10'beta_I':0.200121'beta_H':0.029890'beta_F':0.1'gamma_h':0.330062'gamma_d':0.043827gamma_I':0.05'gamma_f':0.25'delta_1':.55'delta_2':.55'dx':0.6
• 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
25
APPENDIXSupporting material describing model structure, and additional results
26
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
27
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: 610–21. doi:10.1017/S0950268806007217.
28
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.
29
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
30
Parameters of two historical outbreaks
31
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
32
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
33
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
34
Liberia model params
35
Sierra Leone model params
36
All Countries model params
37
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