Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, USA; Bruno Kessler Foundation (FBK), Trento, Italy.
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, USA.
Epidemics. 2018 Mar;22:3-12. doi: 10.1016/j.epidem.2017.09.001. Epub 2017 Sep 20.
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.
由福格蒂国际中心的传染病动力学研究和政策（RAPIDD）项目组织的埃博拉预测挑战赛依赖于通过对高度详细的空间结构基于代理的模型的数值模拟生成的合成疾病数据集。在这里，我们讨论了挑战的架构和技术步骤，这些数据集尽可能地模拟了 2014-2015 年西非埃博拉疫情期间的数据收集、报告和通信过程。我们详细讨论了模型的定义、流行病学情景的构建、合成患者数据库的生成以及挑战赛期间使用的数据通信平台。最后，我们就将合成挑战扩展和扩展到其他传染病的考虑因素和收获提供了一些考虑。
