防范大流行病统计模型的互可互操作性:原则和现实 (Interoperability of statistical models in pandemic preparedness: principles and reality)

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

from arxiv, 26 pages, 10 figures, for associated mpeg file Movie 1 please see https://www.dropbox.com/s/kn9y1v6zvivfla1/Interoperability_of_models_Movie_1.mp4?dl=0

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 spatial-temporal coronavirus disease 2019 (COVID-19) prevalence and reproduction numbers in England.

翻译：我们提出“互操作性”作为统计建模的指导框架,以协助决策者在面对不断演变的大流行病反应时利用不同的数据集提出多种问题。互操作性为今后的大流行病防备提供了一套重要原则,通过联合设计和部署适应性强的疾病监测统计模型系统,利用概率推理进行疾病监测。我们通过案例研究来推断英格兰2019年空间时空冠状病毒(COVID-19)的流行和复制数字来说明这一点。