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为传染病传播建模做好准备。

Tooling-up for infectious disease transmission modelling.

机构信息

School of Public Health, Infectious Disease Epidemiology, Imperial College London, United Kingdom.

Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.

出版信息

Epidemics. 2020 Sep;32:100395. doi: 10.1016/j.epidem.2020.100395. Epub 2020 May 13.

Abstract

In this introduction to the Special Issue on methods for modelling of infectious disease epidemiology we provide a commentary and overview of the field. We suggest that the field has been through three revolutions that have focussed on specific methodological developments; disease dynamics and heterogeneity, advanced computing and inference, and complexity and application to the real-world. Infectious disease dynamics and heterogeneity dominated until the 1980s where the use of analytical models illustrated fundamental concepts such as herd immunity. The second revolution embraced the integration of data with models and the increased use of computing. From the turn of the century an emergence of novel datasets enabled improved modelling of real-world complexity. The emergence of more complex data that reflect the real-world heterogeneities in transmission resulted in the development of improved inference methods such as particle filtering. Each of these three revolutions have always kept the understanding of infectious disease spread as its motivation but have been developed through the use of new techniques, tools and the availability of data. We conclude by providing a commentary on what the next revoluition in infectious disease modelling may be.

摘要

在本期传染病流行病学建模方法特刊的引言中,我们对该领域进行了评论和概述。我们认为,该领域经历了三次革命,这些革命集中在特定的方法发展上:疾病动力学和异质性、先进的计算和推理,以及复杂性和对现实世界的应用。传染病动力学和异质性一直主导着该领域,直到 20 世纪 80 年代,分析模型的使用才说明了群体免疫等基本概念。第二次革命则是将数据与模型结合起来,更多地利用计算。从本世纪初开始,新数据集的出现使对现实世界复杂性的建模得到了改进。更复杂的数据的出现反映了传播中的现实世界异质性,从而开发出了改进的推理方法,如粒子滤波。这三次革命中的每一次都始终以理解传染病传播为其动机,但都是通过新技术、工具的使用和数据的可用性来发展的。最后,我们对传染病建模的下一次革命可能是什么进行了评论。

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