评估高分辨率接近度指标在预测COVID-19传播中的效用

IF 1.2 Q4 REMOTE SENSING
Zakaria Mehrab, A. Adiga, M. Marathe, S. Venkatramanan, S. Swarup
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引用次数: 3

摘要

在过去几年中,高分辨率流动性数据集越来越多,并使包括COVID-19在内的传染病传播的详细模型成为可能。然而,关于如何在流行病模型中有效地使用这种流动性数据以及它们最适合哪些任务,还存在一些悬而未决的问题。在本文中,我们从X-Mode的高分辨率手机追踪数据中提取了一些基于图形的接近度量,并使用它来研究美国50个赠地大学县的COVID-19流行病传播。我们提出了一种方法,通过拟合一个基于常微分方程的模型,并使用多元线性回归来解释估计的时变传播率,从而估计迁移率对情况的影响。我们发现,虽然流动性发挥了重要作用,但随后的相关分析表明,各县之间的贡献是异质性的。我们还评估了指标对定义为监督分类问题的病例浪涌预测的效用,并表明学习的模型可以以95%的准确率和87%的f1分数预测浪涌。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating the Utility of High-Resolution Proximity Metrics in Predicting the Spread of COVID-19
High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ordinary differential equation based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We also evaluate the metrics’ utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and an 87% F1-score.
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来源期刊
CiteScore
4.40
自引率
5.30%
发文量
43
期刊介绍: ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.
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