The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation
Authored by Marco Ajelli, Stefano Merler, Alessandro Vespignani, Laura Fumanelli, Gerardo Chowell, Cecile Viboud, Qian Zhang, Kaiyuan Sun, Lone Simonsen
Date Published: 2018
DOI: 10.1016/j.epidem.2017.09.001
Sponsors:
United States National Institutes of Health (NIH)
United States Department of Homeland Security
United States National Science Foundation (NSF)
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Model Code URLs:
Model code not found
Abstract
The Ebola forecasting challenge organized by the Research and Policy for
Infectious Disease Dynamics (RAPIDD) program of the Fogarty
International Center relies on synthetic disease datasets generated by
numerical simulations of a highly detailed spatially-structured
agent-based model. We discuss here the architecture and technical steps
of the challenge, leading to datasets that mimic as much as possible the
data collection, reporting, and communication process experienced in the
2014-2015 West African Ebola outbreak. We provide a detailed discussion
of the model's definition, the epidemiological scenarios' construction,
synthetic patient database generation and the data communication
platform used during the challenge. Finally we offer a number of
considerations and takeaways concerning the extension and scalability of
synthetic challenges to other infectious diseases.
Tags
Computational Modeling
Epidemic
West-africa
Interventions
Impact
Spread
Sierra-leone
Liberia
Forecast
Ebola
Virus disease outbreak
Transmission
dynamics