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

BMC Medicine

Opinion

Stefano Merler¹  Lone Simonsen²  Cécile Viboud³  Alessandro Vespignani⁴  Gerardo Chowell⁵ 

[1]Bruno Kessler Foundation, Trento, Italy

[2]Department of Public Health, University of Copenhagen, Copenhagen, Denmark

[3]Department of Global Health, George Washington University, Washington DC, USA

[4]Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA

[5]Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA

[6]School of Public Health, Georgia State University, Atlanta, GA, USA

[7]Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA

关键词: Ebola;    West Africa;    Epidemic model;    Lessons learned;    Disease forecast;    Exponential growth;    Sub-exponential growth;    Polynomial growth;    Data sharing;   

DOI  :  10.1186/s12916-017-0811-y

 received in 2016-11-18, accepted in 2017-02-07,  发布年份 2017

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2017