Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK; NIHR Health Protection Research Unit in Emerging and Zoonotic Diseases, The Ronald Ross Building, University of Liverpool, 8 West Derby Street, Liverpool L69 7BE, UK; MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK.
MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK.
Epidemics. 2021 Dec;37:100520. doi: 10.1016/j.epidem.2021.100520. Epub 2021 Nov 2.
While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner.
虽然疾病传播的数学模型被广泛用于为全球公共卫生决策者提供信息,但结果中固有的不确定性往往沟通不畅。我们概述了流行模型中一些潜在的不确定性来源,介绍了传统方法用于说明不确定性,并讨论了整个 COVID-19 大流行期间建模组使用的替代表示形式。然后,通过借鉴我们自己最近建模的经验,我们试图通过提供一种如何以清晰简单的方式呈现这种方法的建议,为如何改进传统的不确定性可视化方法的讨论做出贡献。