基于时空卷积序列到序列神经网络的新冠肺炎地理空间演化模型

IF 1.2 Q4 REMOTE SENSING
Mário Cardoso, A. Cavalheiro, Alexandre Borges, A. F. Duarte, A. Soares, M. Pereira, N. Nunes, L. Azevedo, Arlindo L. Oliveira
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引用次数: 3

摘要

欧洲受到COVID-19大流行的严重打击,葡萄牙受到严重影响,前12个月经历了三波疫情。大约在2021年1月19日至2月5日期间,葡萄牙是世界上发病率最高的国家,每10万居民的14天发病率超过1000人。尽管具有重要意义,但准确预测COVID-19的地理空间演变仍然是一项挑战,因为现有的分析方法无法捕捉到一个区域内的传染和感染从受感染的邻近区域传播所造成的复杂动态。我们使用以前开发的方法和葡萄牙卫生总局(DGS)的官方市级数据,相对于大流行的前12个月,计算葡萄牙大陆每个地点的发病率估计数。然后将所得的发病率图序列作为金标准来测试不同方法在预测发病率时空演变方面的有效性。测试了四种不同的方法:简单的细胞水平自回归移动平均(ARMA)模型、细胞水平矢量自回归(VAR)模型、逐市划分的SIRD模型,然后进行直接块序列模拟,以及基于STConvS2S架构的新型卷积序列对序列神经网络模型。我们得出结论,与ARMA、VAR和SIRD模型以及基线ConvLSTM模型相比,改进的卷积序列到序列神经网络是该任务中表现最好的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling the Geospatial Evolution of COVID-19 using Spatio-temporal Convolutional Sequence-to-sequence Neural Networks
Europe was hit hard by the COVID-19 pandemic and Portugal was severely affected, having suffered three waves in the first twelve months. Approximately between January 19th and February 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-day incidence rates per 100,000 inhabitants in excess of 1,000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from the contagion within a region and the spreading of the infection from infected neighboring regions. We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation, and a new convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the modified convolutional sequence-to-sequence neural network is the best performing method in this task, when compared with the ARMA, VAR, and SIRD models, as well as with the baseline ConvLSTM model.
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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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