Perspectives on model forecasts of the 2014–2015 Ebola epidemic in West Africa: lessons and the way forward

Gerardo Chowell, Cécile Viboud, Lone Simonsen, Stefano Merler and Alessandro Vespignani


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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