Using data-driven agent-based models for forecasting emerging infectious diseases.

Using data-driven agent-based models for forecasting emerging infectious diseases.
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使用基于数据驱动的代理模型来预测新兴的传染病。

DOI:
10.1016/j.epidem.2017.02.010
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发表时间:
2018-03
期刊:
影响因子:
3.8
通讯作者:
Marathe M
Marathe M
中科院分区:
医学2区
文献类型:
--
作者:
Venkatramanan S;Lewis B;Chen J;Higdon D;Vullikanti A;Marathe M

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为持续的紧急疾病流行而生产及时,信息良好且可靠的林业者是一个巨大的挑战。在此设置下的各种干预措施中疾病的动态和社会行为可能导致更好的理解和行动模型的各种组成部分,校准过程和总结挑战方案的预测性能。可以完善和适应以后的情节,并在挑战过程中分享所学的教训。
Producing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models provide a comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper, we describe one such agent-based model framework developed for forecasting the 2014–15 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refined and adapted for future epidemics, and share the lessons learned over the course of the challenge.
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