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 of
spatiotemporal heterogeneity in the case fatality ratios (CFR).
Methods: We applied a non-parametric Boosted Regression Trees (BRT) imputation approach for patients
with missing survival outcomes and adjusted for model imperfection. Semivariogram analysis and
kriging were used to investigate spatiotemporal heterogeneities.
Results: CFR estimates varied significantly between districts and over time over the course of the
epidemic. BRT modelling accounted for most of the spatiotemporal variation and interactions in CFR, but
moderate 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 of
CFR accounting for the both explained and unexplained spatial variation. Temporal autocorrelation was
not 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 Ebola
epidemic with a view of informing future public health contingency planning, resource allocation and
impact assessment. The analytical framework developed in this analysis, coupled with key domain
knowledge, could be deployed in real time to support the response to ongoing and future outbreaks.