Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 114, Issue 9, Pages 2337-2342Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1614595114
Keywords
Ebola; superspreading; offspring distribution; Bayesian inference
Categories
Funding
- Bill & Melinda Gates Foundation [OPP1091919]
- RAPIDD program of the Science and Technology Directorate Department of Homeland Security
- UK Medical Research Council (MRC)
- MRC Career Award in Biostatistics [MR/K021680/1]
- Fogarty International Center, National Institutes of Health
- Medical Research Council [MR/J008761/1, MR/K021680/1, MR/K010174/1B, 1109359] Funding Source: researchfish
- National Institute for Health Research [HPRU-2012-10080] Funding Source: researchfish
- MRC [MR/K021680/1, MR/J008761/1] Funding Source: UKRI
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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 super-spreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion (similar to 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.
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