论文标题
美国共vid-19的矢量量化和分区在美国
Vector quantisation and partitioning of COVID-19 temporal dynamics in the United States
论文作者
论文摘要
人群中病原体的统计动力学取决于一系列因素：人口密度，对社会疏远的有效性和投资，公共政策措施和非药物干预措施（NPI）只是某些因素的示例，这些因素会影响随着时间的推移随时间的流逝。本文概述了使用软dtw（动态时间扭曲）K-均值聚类和基于K形的集群算法来识别案例计数的内部一致性集群，概述了美国的时间序列矢量量化和COVID-19病例的分析。特征类型的时间相关变化的识别可以导致时间序列集中的模式识别。反过来，这可以帮助辨别某个地区传染性动态的未来，并通过计算群集Barycenter来识别最有可能的群集轨迹，从而为公共卫生的决策提供了信息。
The statistical dynamics of a pathogen within a population depend on a range of factors: population density, the effectiveness and investment into social distancing, public policy measures and non-pharmaceutical interventions (NPIs) are only some examples of factors that influence the number of cases over time by state. This paper outlines an analysis of time series vector quantisation and paritioning of COVID-19 cases in the United States, using a soft-DTW (Dynamic Time Warping) k-means clustering and a k-shape based clustering algorithm to identify internally consistent clusters of case counts over time. The identification of characteristic types of time-dependent variations can lead to the identification of patterns within sets of time series. This, in turn, can help discern the future of infectious dynamics in an area and, through identifying the most likely cluster-wise trajectory by calculating the cluster barycenter, inform public health decision-making.
