论文标题
冠状病毒扩散的拓扑数据分析模型
Topological data analysis model for the spread of the coronavirus
论文作者
论文摘要
我们将拓扑数据分析（特别是映射算法）应用于美国Covid-19数据。所得的映射图提供了大流行的可视化，比其他更标准的方法提供的大流行更完整。它们编码由地理信息，时间进程和COVID-19案例的数量创建的数据云的各种几何特征。它们反映了大流行在整个美国的发展，并捕捉了热点的增长率以及区域的突出。映射器图可以在时间和空间上轻松进行比较，并具有成为冠状病毒传播的有用预测工具的潜力。
We apply topological data analysis, specifically the Mapper algorithm, to the U.S. COVID-19 data. The resulting Mapper graphs provide visualizations of the pandemic that are more complete than those supplied by other, more standard methods. They encode a variety of geometric features of the data cloud created from geographic information, time progression, and the number of COVID-19 cases. They reflect the development of the pandemic across all of the U.S. and capture the growth rates as well as the regional prominence of hot-spots. The Mapper graphs allow for easy comparisons across time and space and have the potential of becoming a useful predictive tool for the spread of the coronavirus.
