论文标题
数据驱动的Covid-2019的分析模型，用于流行性预测，临床诊断，政策有效性和接触跟踪：一项调查
Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey
论文作者
论文摘要
广泛传播的冠状病毒（Covid）-19是历史上最严重的传染病爆发之一，已成为主要国际关注的紧急情况。随着大流行的发展，学术社区已经积极参与各种能力，包括准确的流行估计，快速临床诊断，政策有效性评估和合同追踪技术的发展。关于COVID-19的爆发有23,000多个学术论文，这一数字每20天增加一倍，而大流行仍在进行中[1]。然而，从数据分析的角度来看，文献在其早期阶段缺乏全面的调查。在本文中，我们回顾了分析COVID19相关数据，进行出版后模型评估和跨模型比较的最新模型，并从不同的项目中收集数据源。
The widely spread CoronaVirus Disease (COVID)-19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.
