Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021)最新文献

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Correlation Analysis of Spatio-temporal Arabic COVID-19 Tweets 阿拉伯语COVID-19推文时空相关性分析

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021) Pub Date : 2021-11-02 DOI: 10.1145/3486633.3491092

Tarek Elsaka, Imad Afyouni, I. A. Hashem, Z. Aghbari

{"title":"Correlation Analysis of Spatio-temporal Arabic COVID-19 Tweets","authors":"Tarek Elsaka, Imad Afyouni, I. A. Hashem, Z. Aghbari","doi":"10.1145/3486633.3491092","DOIUrl":"https://doi.org/10.1145/3486633.3491092","url":null,"abstract":"Since the recent COVID-19 outbreak, several researchers have begun to focus on various difficulties to data mining of social data to study people's reactions to the outbreak. Recent approaches have mostly concentrated on the analysis of social data in the English language. In this study, we present an in-depth social data mining approach to extract Spatio-temporal and semantic insights about the COVID-19 pandemic from the Arabic social data. We developed sentiment-based categorization methods to extract major topics at various location granularities (regions/cities). Besides, we used topic abstraction levels (subtopics and main topics). A correlation-based analysis of Arabic tweets and official health provider data will also be presented. Furthermore, we used occurrence-based and statistical correlation methodologies to create many topic-based analysis mechanisms. Our findings demonstrate a positive association between top subjects (for example, lockdown and vaccine) and the increasing number of COVID-19 new cases, but unfavorable attitudes among Arab Twitter users were generally heightened during this pandemic, on issues such as lockdown, closure, and law enforcement.","PeriodicalId":337679,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130283292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A clustering environment for real-time tracking and analysis of Covid-19 case clusters 构建新型冠状病毒病例聚集性实时跟踪分析的聚类环境

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021) Pub Date : 2021-11-02 DOI: 10.1145/3486633.3491091

Jayakrishnan Ajayakumar, Andrew Curtis, J. Mills

{"title":"A clustering environment for real-time tracking and analysis of Covid-19 case clusters","authors":"Jayakrishnan Ajayakumar, Andrew Curtis, J. Mills","doi":"10.1145/3486633.3491091","DOIUrl":"https://doi.org/10.1145/3486633.3491091","url":null,"abstract":"Spatial surveillance systems can be an effective and efficient way to provide early spatio-temporal warning signals of infectious diseases outbreaks. The development of spatial surveillance systems that can handle near-real time hospital spatial data have proven critical for directing intervention strategies and for risk mitigation during the ongoing monitoring and response to COVID-19. GeoMEDD, a geographic monitoring tool, was developed for such near-real time assessment of emergent localized disease. As the response to COVID-19 changed, moving through phases such as increased scientific understanding, testing variability, vaccine availability and uptake, and new variants, so GeoMEDD has also evolved. Here we present two advances for GeoMEDD; a fully automated cluster environment with a spatial database at its heart and a cluster tracking module to classify clusters based on the transition state of the cluster lifecycle. Our detailed use case analysis shows that these advances have improved local and global cluster analysis, contextual information dissemination, monitoring emergence based on underlying spatial structure and cluster evolution analysis. We believe that the addition of the fully automated cluster environment to GeoMEDD would be particularly beneficial for health institutions as well as governmental health organizations for disease outbreak detection due to the efficiency in data ingestion and analysis, while the addition of the cluster tracking module will advance research into the mechanics behind disease diffusion in space and time.","PeriodicalId":337679,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132440040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Mobility Response to COVID-19-related Restrictions in New York City 纽约市应对covid -19相关限制措施

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology (SpatialEpi 2021) Pub Date : 2021-11-02 DOI: 10.1145/3486633.3491094

Emily Chen, Grant McKenzie

引用次数: 3