The RAPIDD Ebola forecasting challenge special issue: Preface

Epidemics
Volume 22, Pages 1-78 (March 2018)
https://www.sciencedirect.com/journal/epidemics/vol/22/suppl/C

Special Issue: The RAPIDD Ebola Forecasting Challenge
Edited by Gerardo Chowell-Puente, Alessandro Vespignani, Cecile Viboud, Lone Simonsen
The RAPIDD Ebola forecasting challenge special issue: Preface
Open access   Pages 1-2
Cécile Viboud, Lone Simonsen, Gerardo Chowell, Alessandro Vespignani
[Initial text]
Interest in disease forecasting is growing, stimulated by the continued threat of emerging infections, increased modeling capabilities, and a more porous interface between the modeling community and policy experts who make decisions to roll out interventions (Chretien et al., 2015a). Forecasting competitions are common in the field of meteorology and climate, but have just started to percolate the field of disease modeling. Key examples include the multi-year seasonal flu contest initiated by the US CDC in 2015, the dengue challenge organized by NOAA, and the chikungunya challenge led by DARPA (DARPA, 2015; Biggerstaff et al., 2016; NOAA, 2015). In this special issue, we describe the Ebola forecasting challenge led by the US National Institute of Health in 2015–2016, as part of their Research And Policy in Infectious Disease Dynamics (RAPIDD) program.
The RAPIDD Ebola forecasting challenge was launched in the aftermath of the 2014–2015 West African Ebola outbreak, during which a large number of real-time modeling approaches were used to generate useful but sometime conflicting predictions due to the varying outcomes, methodological assumptions and scenarios considered (Chretien et al., 2015b). The goals of this challenge were to compare the prediction accuracy of different Ebola transmission models while exploring a range of data availability, measurement error, and epidemiological complexity in a controlled manner. The Ebola forecasting challenge was unique in that it relied on synthetic outbreak data generated by a detailed mechanistic disease model, while other challenges have relied on empirical outbreak data thus far…
This special issue on Ebola forecasting intersects nicely with previous Special issues of Epidemics on model comparisons for neglected tropical diseases (Hollingsworth and Medley, 2017), and challenges in infectious diseases dynamics (Lloyd-Smith et al., 2015). Common emergent themes include the need for development of standardized methods for model comparisons and validation, handling of uncertainty, availability and accuracy of empirical data, development of portfolio of models tailored to different types of pandemic threats, and the interface between models and policy. Most inspiringly perhaps is the spirit of collegiality and collaboration that characterizes the large group projects described in these three special issues.
In conclusion, we anticipate that the RAPIDD Ebola Forecasting special issue will stimulate further synthetic and empirical infectious disease challenges and systematic model comparisons.    These large-scale projects represent an intense amount of work from participating teams, with typically little or no funding. However, we believe they represent an important step towards improving the link between policy and modeling. Emerging and re-emerging infectious diseases will continue to present new threats, and we hope that this body of work will inspire a new crop of scientists to collaboratively advance the field of infectious disease forecasting…