Imperial College London
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DrIlariaDorigatti

Faculty of Medicine, School of Public Health

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 1451i.dorigatti

 
 
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Location

 

G24Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Forna:2020:10.1016/j.ijid.2020.01.046,
author = {Forna, A and Dorigatti, I and Nouvellet, P and Donnelly, C},
doi = {10.1016/j.ijid.2020.01.046},
journal = {International Journal of Infectious Diseases},
pages = {48--55},
title = {Spatiotemporal variability in case fatality ratios for 2013–2016 Ebola epidemic in West Africa},
url = {http://dx.doi.org/10.1016/j.ijid.2020.01.046},
volume = {93},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background: For the 2013–2016 Ebola epidemic in West Africa, the largest Ebola virus disease (EVD)epidemic to date, we aim to analyse the patient mix in detail to characterise key sources ofspatiotemporal heterogeneity in the case fatality ratios (CFR).Methods: We applied a non-parametric Boosted Regression Trees (BRT) imputation approach for patientswith missing survival outcomes and adjusted for model imperfection. Semivariogram analysis andkriging were used to investigate spatiotemporal heterogeneities.Results: CFR estimates varied significantly between districts and over time over the course of theepidemic. BRT modelling accounted for most of the spatiotemporal variation and interactions in CFR, butmoderate spatial autocorrelation remained for distances up to approximately 90 km. Combining districtlevel CFR estimates and kriged district-level residuals provided the best linear unbiased predicted map ofCFR accounting for the both explained and unexplained spatial variation. Temporal autocorrelation wasnot observed in the district-level residuals from the BRT estimates.Conclusions: This study provides new insight into the epidemiology of the 2013–2016 West African Ebolaepidemic with a view of informing future public health contingency planning, resource allocation andimpact assessment. The analytical framework developed in this analysis, coupled with key domainknowledge, could be deployed in real time to support the response to ongoing and future outbreaks.
AU  - Forna,A
AU  - Dorigatti,I
AU  - Nouvellet,P
AU  - Donnelly,C
DO  - 10.1016/j.ijid.2020.01.046
EP  - 55
PY  - 2020///
SN  - 1201-9712
SP  - 48
TI  - Spatiotemporal variability in case fatality ratios for 2013–2016 Ebola epidemic in West Africa
T2  - International Journal of Infectious Diseases
UR  - http://dx.doi.org/10.1016/j.ijid.2020.01.046
UR  - http://hdl.handle.net/10044/1/77311
VL  - 93
ER  -
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