Suppr超能文献

新冠疫情两年:意大利Statgroup-19的经验

Two years of COVID-19 pandemic: The Italian experience of Statgroup-19.

作者信息

Jona Lasinio Giovanna, Divino Fabio, Lovison Gianfranco, Mingione Marco, Alaimo Di Loro Pierfrancesco, Farcomeni Alessio, Maruotti Antonello

机构信息

Department of Statistical Sciences "La Sapienza" University of Rome Rome Italy.

Department of Bio-Sciences University of Molise Italy.

出版信息

Environmetrics. 2022 Dec;33(8):e2768. doi: 10.1002/env.2768. Epub 2022 Oct 4.

Abstract

The amount and poor quality of available data and the need of appropriate modeling of the main epidemic indicators require specific skills. In this context, the statistician plays a key role in the process that leads to policy decisions, starting with monitoring changes and evaluating risks. The "what" and the "why" of these changes represent fundamental research questions to provide timely and effective tools to manage the evolution of the epidemic. Answers to such questions need appropriate statistical models and visualization tools. Here, we give an overview of the role played by Statgroup-19, an independent Italian research group born in March 2020. The group includes seven statisticians from different Italian universities, each with different backgrounds but with a shared interest in data analysis, statistical modeling, and biostatistics. Since the beginning of the COVID-19 pandemic the group has interacted with authorities and journalists to support policy decisions and inform the general public about the evolution of the epidemic. This collaboration led to several scientific papers and an accrued visibility across various media, all made possible by the continuous interaction across the group members that shared their unique expertise.

摘要

现有数据的数量和质量欠佳,以及对主要疫情指标进行适当建模的需求,都需要特定的技能。在这种情况下,统计学家在通向政策决策的过程中发挥着关键作用,这一过程始于监测变化和评估风险。这些变化的“是什么”和“为什么”是基础研究问题,目的是提供及时有效的工具来管理疫情的演变。回答此类问题需要适当的统计模型和可视化工具。在此,我们概述Statgroup-19所发挥的作用,这是一个2020年3月成立的意大利独立研究小组。该小组包括来自意大利不同大学的七名统计学家,他们各自背景不同,但都对数据分析、统计建模和生物统计学有着共同兴趣。自新冠疫情开始以来,该小组一直与当局和记者互动,以支持政策决策并向公众通报疫情的演变情况。这种合作产生了多篇科学论文,并在各种媒体上获得了更高的知名度,这一切都得益于小组成员之间持续的互动,他们分享了各自独特的专业知识。

相似文献

4
Data sharing and the evolving role of statisticians.数据共享与统计学家不断演变的角色。
BMC Med Res Methodol. 2016 Jul 8;16 Suppl 1(Suppl 1):75. doi: 10.1186/s12874-016-0172-9.
6
Covid-19 in Italy: Modelling, communications, and collaborations.意大利的新冠疫情:建模、沟通与合作。
Signif (Oxf). 2022 Apr;19(2):19-21. doi: 10.1111/1740-9713.01629. Epub 2022 Mar 29.

本文引用的文献

6
Inferring the effectiveness of government interventions against COVID-19.推断政府干预 COVID-19 的效果。
Science. 2021 Feb 19;371(6531). doi: 10.1126/science.abd9338. Epub 2020 Dec 15.
8
Ranking the effectiveness of worldwide COVID-19 government interventions.对全球 COVID-19 政府干预措施的效果进行排名。
Nat Hum Behav. 2020 Dec;4(12):1303-1312. doi: 10.1038/s41562-020-01009-0. Epub 2020 Nov 16.
9
Forecasting for COVID-19 has failed.对新冠疫情的预测失败了。
Int J Forecast. 2022 Apr-Jun;38(2):423-438. doi: 10.1016/j.ijforecast.2020.08.004. Epub 2020 Aug 25.
10
Using Phenomenological Models to Characterize Transmissibility and Forecast Patterns and Final Burden of Zika Epidemics.使用现象学模型来表征寨卡疫情的传播性、预测模式及最终负担。
PLoS Curr. 2016 May 31;8:ecurrents.outbreaks.f14b2217c902f453d9320a43a35b9583. doi: 10.1371/currents.outbreaks.f14b2217c902f453d9320a43a35b9583.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验