Data Science in Times of Pan(dem)ic
Data Science in Times of Pan(dem)ic
复制标题
大流行时代的数据科学
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
S. Leonelli
中科院分区：
文献类型：
--
作者：
S. Leonelli
在应对COVID-19方面，数据科学的优先事项是什么？大数据分析可以通过哪些方式为应对疫情提供信息和支持？数据科学家必须花费时间和资源来确定、审查和质疑其工作的可能使用场景，特别是考虑到冠状病毒大流行等紧急情况所需的快节奏知识生产。在本文中，我通过确定数据科学对大流行应对的贡献的五种方式来想象和预测未来，并反思这些方法如何为科学研究领域内外的投资和优先事项的当前分配提供信息，为这些考虑提供了一个框架。其中前两个领域，包括（1）人口监测和（2）预测建模，主导了第一波政府和科学反应，对研究和社会都有潜在的问题影响。我认为，更多地强调后三个方面，包括（3）因果解释，（4）物流决策的评估和（5）社会和环境需求的确定，将为利用数据科学支持人类与冠状病毒共存提供一个更平衡，可持续和负责任的途径。
What are the priorities for data science in tackling COVID-19 and in which ways can big data analysis inform and support responses to the outbreak? It is imperative for data scientists to spend time and resources scoping, scrutinizing and questioning the possible scenarios of use of their work – particularly given the fast-paced knowledge production required by an emergency situation such as the coronavirus pandemic. In this paper I provide a scaffold for such considerations by identifying five ways in which the data science contributions to the pandemic response are imagined and projected into the future, and reflecting on how such imaginaries inform current allocations of investment and priorities within and beyond the scientific research landscape. The first two of these imaginaries, which consist of (1) population surveillance and (2) predictive modelling, have dominated the first wave of governmental and scientific responses with potentially problematic implications for both research and society. Placing more emphasis on the latter three imaginaries, which include (3) causal explanation, (4) evaluation of logistical decisions and (5) identification of social and environmental need, I argue, would provide a more balanced, sustainable and responsible avenue towards using data science to support human co-existence with coronavirus.
循证政策：做得更好的实用指南
DOI：
--
发表时间：
2012
期刊：
--
影响因子：
--
作者：
Cartwright
通讯作者：
Cartwright