Pub Date : 2020-11-12DOI: 10.1101/2020.11.08.20227322
A. Puls, S. Bracke
In December 2019, the world was confronted with the outbreak of the respiratory disease COVID-19 (Corona). The first infection (confirmed case) was detected in the City Wuhan, Hubei, China. First, it was an epidemic in China, but in the first quarter of 2020, it evolved into a pandemic, which continues to this day. The COVID-19 pandemic with its incredible speed of spread shows the vulnerability of a globalized and networked world. The first months of the pandemic were characterized by heavy burden on health systems. Worldwide, the population of countries was affected with severe restrictions, like educational system shutdown, public traffic system breakdown or a comprehensive lockdown. The severity of the burden was dependent on many factors, e.g. government, culture or health system. However, the burden happened regarding each country with slight time lags, cf. Bracke et al. (2020). This paper focuses on data analytics regarding infection data of the COVID-19 pandemic. It is a continuation of the research study COVID-19 pandemic data analytics: Data heterogeneity, spreading behavior, and lockdown impact, published by Bracke et al. (2020). The goal of this assessment is the evaluation/analysis of infection data mining considering model uncertainty, pandemic spreading behavior with lockdown impact and early second wave in Germany, Italy, Japan, New Zealand and France. Furthermore, a comparison with other infectious diseases (measles and influenza) is made. The used data base from Johns Hopkins University (JHU) runs from 01/22/2020 until 09/22/2020 with daily data, the dynamic development after 09/22/2020 is not considered. The measles/influenza analytics are based on Robert Koch Institute (RKI) data base 09/22/2020. Statistical models and methods from reliability engineering like Weibull distribution model or trend test are used to analyze the occurrence of infection.
2019年12月，全球面临2019冠状病毒病(COVID-19)疫情。首例感染(确诊病例)是在中国湖北省武汉市发现的。首先，这是中国的一场流行病，但在2020年第一季度，它演变成一场大流行，一直持续到今天。COVID-19大流行以其惊人的传播速度显示了全球化和网络化世界的脆弱性。大流行的头几个月的特点是卫生系统负担沉重。在世界范围内，各国人口受到严重限制的影响，如教育系统关闭、公共交通系统崩溃或全面封锁。这种负担的严重程度取决于许多因素，例如政府、文化或卫生系统。然而，每个国家的负担都有轻微的时间滞后，参见Bracke et al.(2020)。本文主要对COVID-19大流行的感染数据进行数据分析。这是布拉克等人(2020年)发表的《2019冠状病毒病大流行数据分析:数据异质性、传播行为和封锁影响》研究的延续。本次评估的目标是评估/分析感染数据挖掘，考虑模型不确定性、具有封锁影响的大流行传播行为和德国、意大利、日本、新西兰和法国的第二波早期。此外，还与其他传染病(麻疹和流感)进行了比较。使用的约翰霍普金斯大学(JHU)数据库的运行时间为2020年1月22日至2020年9月22日，每日数据，不考虑2020年9月22日之后的动态发展。麻疹/流感分析基于罗伯特·科赫研究所(RKI) 2020年9月22日的数据库。使用威布尔分布模型或趋势检验等可靠性工程的统计模型和方法来分析感染的发生。
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Pub Date : 2020-11-11DOI: 10.1007/978-3-030-56178-9_6
Zhijiao Xiao, Jianmin Jiang, Zhong Ming
{"title":"High Level Video Event Modeling, Recognition and Reasoning via Petri Net","authors":"Zhijiao Xiao, Jianmin Jiang, Zhong Ming","doi":"10.1007/978-3-030-56178-9_6","DOIUrl":"https://doi.org/10.1007/978-3-030-56178-9_6","url":null,"abstract":"","PeriodicalId":163967,"journal":{"name":"Studies in computational intelligence","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131421034","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}
Pub Date : 2020-08-25DOI: 10.1007/978-94-007-1917-0_18
S. Dong
{"title":"Conclusions and Outlooks","authors":"S. Dong","doi":"10.1007/978-94-007-1917-0_18","DOIUrl":"https://doi.org/10.1007/978-94-007-1917-0_18","url":null,"abstract":"","PeriodicalId":163967,"journal":{"name":"Studies in computational intelligence","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131900423","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}
Pub Date : 2020-08-06DOI: 10.1007/978-3-030-49728-6_27
Kantika Khanthawithoon, Paravee Maneejuk, W. Yamaka
{"title":"Analyzing the Relationship Among Aging Society, Investment in Artificial Intelligence and Economic Growth","authors":"Kantika Khanthawithoon, Paravee Maneejuk, W. Yamaka","doi":"10.1007/978-3-030-49728-6_27","DOIUrl":"https://doi.org/10.1007/978-3-030-49728-6_27","url":null,"abstract":"","PeriodicalId":163967,"journal":{"name":"Studies in computational intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129609578","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}
Pub Date : 2020-08-06DOI: 10.1007/978-3-030-49728-6_8
O. Kosheleva, V. Kreinovich, K. Autchariyapanitkul
{"title":"Why Beta Priors: Invariance-Based Explanation","authors":"O. Kosheleva, V. Kreinovich, K. Autchariyapanitkul","doi":"10.1007/978-3-030-49728-6_8","DOIUrl":"https://doi.org/10.1007/978-3-030-49728-6_8","url":null,"abstract":"","PeriodicalId":163967,"journal":{"name":"Studies in computational intelligence","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115406152","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}
Pub Date : 2020-08-06DOI: 10.1007/978-3-030-49728-6_13
I. Neumann, V. Kreinovich, T. N. Nguyen
{"title":"How to Gauge a Combination of Uncertainties of Different Type: General Foundations","authors":"I. Neumann, V. Kreinovich, T. N. Nguyen","doi":"10.1007/978-3-030-49728-6_13","DOIUrl":"https://doi.org/10.1007/978-3-030-49728-6_13","url":null,"abstract":"","PeriodicalId":163967,"journal":{"name":"Studies in computational intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129969130","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}
Pub Date : 2020-08-06DOI: 10.1007/978-3-030-49728-6_9
O. Kosheleva, V. Kreinovich, Guo Wei
{"title":"Ranking-Based Voting Revisited: Maximum Entropy Approach Leads to Borda Count (and Its Versions)","authors":"O. Kosheleva, V. Kreinovich, Guo Wei","doi":"10.1007/978-3-030-49728-6_9","DOIUrl":"https://doi.org/10.1007/978-3-030-49728-6_9","url":null,"abstract":"","PeriodicalId":163967,"journal":{"name":"Studies in computational intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124964502","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}
Pub Date : 2020-06-22DOI: 10.1007/978-981-15-6325-6
Utku Kose, Omer Deperlioglu, J. Alzubi, B. Patrut
{"title":"Deep Learning for Medical Decision Support Systems","authors":"Utku Kose, Omer Deperlioglu, J. Alzubi, B. Patrut","doi":"10.1007/978-981-15-6325-6","DOIUrl":"https://doi.org/10.1007/978-981-15-6325-6","url":null,"abstract":"","PeriodicalId":163967,"journal":{"name":"Studies in computational intelligence","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131409786","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}
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