Characterizing Covid Waves via Spatio-Temporal Decomposition
Characterizing Covid Waves via Spatio-Temporal Decomposition
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DOI：
10.1145/3534678.3539136
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
K.L. Quinn;Evimaria Terzi;M. Crovella
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文献类型：
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作者：
K.L. Quinn;Evimaria Terzi;M. Crovella
在本文中，我们开发了一个用于分析疾病或流行病（例如新冠肺炎）模式的框架。给定一个记录疾病在一组位置上传播信息的数据集，我们考虑识别疾病的内在波（时间模式）及其各自的空间震中的问题。为此，我们引入了一种新的时空分解方法，称为扩散 NMF (D-NMF)。基于经典的矩阵分解方法，D-NMF 考虑了数据中位置（特征）的空间结构，并支持空间上接近的位置更有可能经历同一组波浪的想法。为了说明 D-NMF 的使用，我们分析了各种空间粒度的 Covid 病例数据。我们的结果表明，D-NMF 在分离流行病的波次和确定每个波次的几个中心方面非常有用。
In this paper we develop a framework for analyzing patterns of a disease or pandemic such as Covid. Given a dataset which records information about the spread of a disease over a set of locations, we consider the problem of identifying both the disease's intrinsic waves (temporal patterns) and their respective spatial epicenters. To do so we introduce a new method of spatio-temporal decomposition which we call diffusion NMF (D-NMF). Building upon classic matrix factorization methods, D-NMF takes into consideration a spatial structuring of locations (features) in the data and supports the idea that locations which are spatially close are more likely to experience the same set of waves. To illustrate the use of D-NMF, we analyze Covid case data at various spatial granularities. Our results demonstrate that D-NMF is very useful in separating the waves of an epidemic and identifying a few centers for each wave.