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Case fatality ratio estimates for the 2013 – 2016 West African Ebola epidemic: application of Boosted Regression Trees for imputation

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Title: Case fatality ratio estimates for the 2013 – 2016 West African Ebola epidemic: application of Boosted Regression Trees for imputation
Authors: Forna, A
Nouvellet, P
Dorigatti, I
Donnelly, C
Item Type: Journal Article
Abstract: Background The 2013-2016 West African Ebola epidemic has been the largest to date with more than 11,000 deaths in the affected countries. The data collected have provided more insight than ever before into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naïve CFR remain limited because 44% of survival outcomes were unreported. Methods Using a Boosted Regression Tree (BRT) model, we imputed survival outcomes (i.e. survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR. Results The out-of-sample performances of our model were good: sensitivity=69.7% (95% CI 52.5%-75.6%), specificity=69.8% (95% CI 54.1%-75.6%), percentage correctly classified=69.9% (95% CI 53.7%-75.5%) and area under the ROC curve= 76.0% (95% CI 56.8%-82.1%). The adjusted CFR estimates for the 2013-2016 West African epidemic were 82.8% (95% CI 45%.6-85.6%) overall and 89.1% (95% CI 40.8%-91.6%) , 65.6% (95% CI 61.3%-69.6%) and 79.2% (95% CI 45.4-84.1) for Sierra Leone, Guinea and Liberia, respectively. We found that district, hospitalisation status, age, case classification and quarter explained 93.6% of the variance in the naïve CFR. Conclusions The adjusted CFR estimates improved the naïve CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemic, and help better allocate resources and evaluate the effectiveness of future inventions.
Issue Date: 22-Jul-2019
Date of Acceptance: 17-Jul-2019
URI: http://hdl.handle.net/10044/1/72313
DOI: https://dx.doi.org/10.1093/cid/ciz678
ISSN: 1058-4838
Publisher: Oxford University Press (OUP)
Journal / Book Title: Clinical Infectious Diseases
Copyright Statement: © The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Sponsor/Funder: Medical Research Council (MRC)
National Institute for Health Research
Funder's Grant Number: MR/R015600/1
HPRU-2012-10080
Keywords: Imputation
Infectious Disease Epidemiology
Machine Learning
Survival
Viral Haemorrhagic Disease
06 Biological Sciences
11 Medical and Health Sciences
Microbiology
Publication Status: Published online
Online Publication Date: 2019-07-22
Appears in Collections:Epidemiology, Public Health and Primary Care



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