Journal
BMC MEDICINE
Volume 15, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s12916-017-0811-y
Keywords
Ebola; West Africa; Epidemic model; Lessons learned; Disease forecast; Exponential growth; Sub-exponential growth; Polynomial growth; Data sharing
Categories
Funding
- NSF [1414374, 1518939, 1318788, 1610429]
- UK Biotechnology and Biological Sciences Research Council [BB/M008894/1]
- RAPIDD Program of the Science AMP
- Technology Directorate
- Division of International Epidemiology and Population Studies
- The Fogarty International Center
- US National Institutes of Health
- European Commission
- Lundbeck Foundation
- NIH [MIDAS-U54GM111274]
- BBSRC [BB/M008894/1] Funding Source: UKRI
- Direct For Biological Sciences
- Division Of Environmental Biology [1414374] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1518939] Funding Source: National Science Foundation
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The unprecedented impact and modeling efforts associated with the 2014-2015 Ebola epidemic in West Africa provides a unique opportunity to document the performances and caveats of forecasting approaches used in near-real time for generating evidence and to guide policy. A number of international academic groups have developed and parameterized mathematical models of disease spread to forecast the trajectory of the outbreak. These modeling efforts often relied on limited epidemiological data to derive key transmission and severity parameters, which are needed to calibrate mechanistic models. Here, we provide a perspective on some of the challenges and lessons drawn from these efforts, focusing on (1) data availability and accuracy of early forecasts; (2) the ability of different models to capture the profile of early growth dynamics in local outbreaks and the importance of reactive behavior changes and case clustering; (3) challenges in forecasting the long-term epidemic impact very early in the outbreak; and (4) ways to move forward. We conclude that rapid availability of aggregated population-level data and detailed information on a subset of transmission chains is crucial to characterize transmission patterns, while ensemble-forecasting approaches could limit the uncertainty of any individual model. We believe that coordinated forecasting efforts, combined with rapid dissemination of disease predictions and underlying epidemiological data in shared online platforms, will be critical in optimizing the response to current and future infectious disease emergencies.
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