论文标题
用拓扑数据分析球映射可视化英语covid-19案例的演变
Visualising the Evolution of English Covid-19 Cases with Topological Data Analysis Ball Mapper
论文作者
论文摘要
通过数据可视化了解疾病扩散已集中在趋势和地图上。尽管这些很有帮助，但它们忽略了社区特征之间重要的多维相互作用。使用拓扑数据分析球映射器算法，我们构建了NUTS3级经济数据的抽象表示，叠加在英格兰确认的Covid-19案例。通过这样做，我们可能会了解该疾病如何在不同的社会经济方面传播。据观察，特征空间的某些区域已迅速竞争到最高水平的感染水平，而在特征空间附近的其他区域则不会显示出大量的感染生长。同样，我们看到在非常不同的区域中出现的模式，这些模式可以指挥更多监视。在理解动态流行数据方面，尤其是对拓扑数据分析和球映射器算法的强大贡献。
Understanding disease spread through data visualisation has concentrated on trends and maps. Whilst these are helpful, they neglect important multi-dimensional interactions between characteristics of communities. Using the Topological Data Analysis Ball Mapper algorithm we construct an abstract representation of NUTS3 level economic data, overlaying onto it the confirmed cases of Covid-19 in England. In so doing we may understand how the disease spreads on different socio-economical dimensions. It is observed that some areas of the characteristic space have quickly raced to the highest levels of infection, while others close by in the characteristic space, do not show large infection growth. Likewise, we see patterns emerging in very different areas that command more monitoring. A strong contribution for Topological Data Analysis, and the Ball Mapper algorithm especially, in comprehending dynamic epidemic data is signposted.
