Interoperability of statistical models in pandemic preparedness: principles and reality.

IF 3.9 1区数学 Q1 STATISTICS & PROBABILITY

Statistical Science Pub Date : 2022-05-01 DOI:10.1214/22-STS854

George Nicholson, Marta Blangiardo, Mark Briers, Peter J Diggle, Tor Erlend Fjelde, Hong Ge, Robert J B Goudie, Radka Jersakova, Ruairidh E King, Brieuc C L Lehmann, Ann-Marie Mallon, Tullia Padellini, Yee Whye Teh, Chris Holmes, Sylvia Richardson

引用次数: 0

Abstract

We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.

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大流行病防备中统计模型的互操作性：原则与现实。

我们将互操作性作为统计建模的指导框架，以帮助决策者在面对不断变化的大流行病应对措施时利用不同的数据集提出多个问题。通过利用概率推理联合设计和部署用于疾病监测的适应性统计模型系统，互操作性为未来的大流行病防备工作提供了一套重要原则。我们通过对英格兰 SARS-CoV-2 感染的时空流行率和繁殖数量进行推断和描述的案例研究来说明这一点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Science 数学-统计学与概率论

CiteScore

6.50

自引率

1.80%

发文量

审稿时长

>12 weeks

期刊介绍： The central purpose of Statistical Science is to convey the richness, breadth and unity of the field by presenting the full range of contemporary statistical thought at a moderate technical level, accessible to the wide community of practitioners, researchers and students of statistics and probability.