title: Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa: lessons and the way forward.
abstract: 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.
authors.affiliation: School of Public Health, Georgia State University, Atlanta, GA, USA. gchowell@gsu.edu.|Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA. gchowell@gsu.edu.
authors.forename: Gerardo
authors.initials: G
authors.lastname: Chowell
authors.affiliation: Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Cécile
authors.initials: C
authors.lastname: Viboud
authors.affiliation: Department of Public Health, University of Copenhagen, Copenhagen, Denmark.|Department of Global Health, George Washington University, Washington DC, USA.
authors.forename: Lone
authors.initials: L
authors.lastname: Simonsen
authors.affiliation: Bruno Kessler Foundation, Trento, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Merler
authors.affiliation: Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Vespignani
mesh_terms: D000354:Africa, Western / Q000453:epidemiology; D058872:Epidemics; D004812:Epidemiologic Methods; D005544:Forecasting; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology* / Q000635:transmission; D006801:Humans; D008954:Models, Biological
keywords: Data sharing; Disease forecast; Ebola; Epidemic model; Exponential growth; Lessons learned; Polynomial growth; Sub-exponential growth; West Africa