The unprecedented scale of the Ebola outbreak in Western
Africa (2014–2015) has prompted an explosion of efforts to
understand the transmission dynamics of the virus and to analyze
the performance of possible containment strategies. Models
have focused primarily on the reproductive numbers of the
disease that represent the average number of secondary infections
produced by a random infectious individual. However,
these population-level estimates may conflate important systematic
variation in the number of cases generated by infected
individuals, particularly found in spatially localized transmission
and superspreading events. Although superspreading features
prominently in first-hand narratives of Ebola transmission, its
dynamics have not been systematically characterized, hindering
refinements of future epidemic predictions and explorations of
targeted interventions. We used Bayesian model inference to integrate
individual-level spatial information with other epidemiological
data of community-based (undetected within clinical-care
systems) cases and to explicitly infer distribution of the cases generated
by each infected individual. Our results show that superspreaders
play a key role in sustaining onward transmission of
the epidemic, and they are responsible for a significant proportion
(∼61%) of the infections. Our results also suggest age as a
key demographic predictor for superspreading. We also show that
community-based cases may have progressed more rapidly than
those notified within clinical-care systems, and most transmission
events occurred in a relatively short distance (with median value
of 2.51 km). Our results stress the importance of characterizing
superspreading of Ebola, enhance our current understanding of
its spatiotemporal dynamics, and highlight the potential importance
of targeted control measures.