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用于沟通建模的视觉偏好:对新冠疫情政策及决策者的全球分析

Visual preferences for communicating modelling: a global analysis of COVID-19 policy and decision makers.

作者信息

Hadley Liza, Rich Caylyn, Tasker Alex, Restif Olivier, Funk Sebastian

机构信息

Disease Dynamics Unit, University of Cambridge, Cambridge, UK.

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

出版信息

Infect Dis Model. 2025 Apr 23;10(3):924-934. doi: 10.1016/j.idm.2025.04.005. eCollection 2025 Sep.

Abstract

Effective communication of modelling results to policy and decision makers has been a longstanding challenge in times of crises. This communication takes many forms - visualisations, reports, presentations - and requires careful consideration to ensure accurate maintenance of the key scientific messages. Science-to-policy communication is further exacerbated when presenting fundamentally uncertain forms of science such as infectious disease modelling and other types of modelled evidence, something which has been understudied. Here we assess the communication and visualisation of infectious disease modelling results to national COVID-19 policy and decision makers in 13 different countries. We present a synthesis of recommendations on what aspects of visuals, graphs, and plots policymakers found to be most helpful in their COVID-19 response work. This work serves as a first evidence base for developing guidelines on the communication and translation of infectious disease modelling into policy.

摘要

在危机时期,将建模结果有效地传达给政策制定者和决策者一直是一项长期挑战。这种沟通有多种形式——可视化、报告、演示——并且需要仔细考虑以确保关键科学信息的准确传达。当呈现本质上具有不确定性的科学形式(如传染病建模和其他类型的建模证据)时,科学与政策之间的沟通会进一步加剧,而这方面的研究还很少。在此,我们评估了在13个不同国家向国家新冠疫情政策和决策者传达传染病建模结果及进行可视化展示的情况。我们综合了政策制定者认为在其新冠疫情应对工作中最有帮助的视觉效果、图表和绘图等方面的建议。这项工作为制定将传染病建模转化为政策的沟通和转化指南提供了首个证据基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2588/12088752/a60cb1552c29/gr1.jpg

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