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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.
OriginalsprogEngelsk
TidsskriftEpidemics
Vol/bind22
Sider (fra-til)3-12
ISSN1755-4365
DOI
StatusUdgivet - 2018
Udgivet eksterntJa

Citer dette

Ajelli, M., Zhang, Q., Sun, K., Merler, S., Fumanelli, L., Chowell, G., ... Vespignani, A. (2018). The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation. Epidemics, 22, 3-12. https://doi.org/10.1016/j.epidem.2017.09.001
Ajelli, Marco ; Zhang, Qian ; Sun, Kaiyuan ; Merler, Stefano ; Fumanelli, Laura ; Chowell, Gerardo ; Simonsen, Lone ; Viboud, Cecile ; Vespignani, Alessandro. / The RAPIDD Ebola forecasting challenge : Model description and synthetic data generation. I: Epidemics. 2018 ; Bind 22. s. 3-12.
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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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Ajelli, M, Zhang, Q, Sun, K, Merler, S, Fumanelli, L, Chowell, G, Simonsen, L, Viboud, C & Vespignani, A 2018, 'The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation' Epidemics, bind 22, s. 3-12. https://doi.org/10.1016/j.epidem.2017.09.001

The RAPIDD Ebola forecasting challenge : Model description and synthetic data generation. / Ajelli, Marco; Zhang, Qian; Sun, Kaiyuan; Merler, Stefano; Fumanelli, Laura; Chowell, Gerardo; Simonsen, Lone; Viboud, Cecile; Vespignani, Alessandro.

I: Epidemics, Bind 22, 2018, s. 3-12.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - The RAPIDD Ebola forecasting challenge

T2 - Model description and synthetic data generation

AU - Ajelli, Marco

AU - Zhang, Qian

AU - Sun, Kaiyuan

AU - Merler, Stefano

AU - Fumanelli, Laura

AU - Chowell, Gerardo

AU - Simonsen, Lone

AU - Viboud, Cecile

AU - Vespignani, Alessandro

PY - 2018

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N2 - 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.

AB - 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.

U2 - 10.1016/j.epidem.2017.09.001

DO - 10.1016/j.epidem.2017.09.001

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JO - Epidemics

JF - Epidemics

SN - 1755-4365

ER -