The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation

Marco Ajelli, Qian Zhang, Kaiyuan Sun, Stefano Merler, Laura Fumanelli, Gerardo Chowell, Lone Simonsen, Cecile Viboud, Alessandro Vespignani

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.

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