Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 2

IF 1.2 Q4 REMOTE SENSING

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2022-10-29 DOI:10.1145/3568669

Andreas Züfle, T. Anderson, Song Gao

{"title":"Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 2","authors":"Andreas Züfle, T. Anderson, Song Gao","doi":"10.1145/3568669","DOIUrl":null,"url":null,"abstract":"Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the five articles included in the second volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to close contact modeling, infectious diseases spread prediction, mobility analysis, effective testing and intervention strategies. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Spatial Algorithms and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3568669","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"REMOTE SENSING","Score":null,"Total":0}

引用次数: 0

Abstract

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the five articles included in the second volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to close contact modeling, infectious diseases spread prediction, mobility analysis, effective testing and intervention strategies. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.

查看原文本刊更多论文

了解新冠肺炎传播特刊导言（下）

传染病在人类宿主之间通过空间和时间的密切接触传播。最近，提供了前所未有的空间和时空数据，可用于提高我们对新冠肺炎和其他传染病传播的理解。这一理解对于通过空间算法和系统为未来的流行病做好准备至关重要，这些算法和系统用于收集、捕获、策划和分析复杂的、多尺度的人类活动数据，以解决传染病预测、接触者追踪和风险评估等问题。在探索和深化围绕这一主题的对话时，本期特刊第二卷中的五篇文章采用了不同的理论视角、方法论和框架，包括但不限于密切接触者建模、传染病传播预测、流动性分析、有效检测和干预策略。这些文章没有关注一系列狭隘的问题，而是让我们一窥利用空间和时空数据做好疫情准备的各种可能性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

ACM Transactions on Spatial Algorithms and Systems REMOTE SENSING-

CiteScore

4.40

自引率

5.30%

发文量

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.