基于agent模拟的COVID-19病例报告数据合成复制与增强

Q2 Computer Science

Data Science Journal Pub Date : 2020-11-10 DOI:10.1101/2020.11.07.20227462

N. Popper, M. Zechmeister, D. Brunmeir, C. Rippinger, N. Weibrecht, C. Urach, M. Bicher, G. Schneckenreither, A. Rauber

{"title":"基于agent模拟的COVID-19病例报告数据合成复制与增强","authors":"N. Popper, M. Zechmeister, D. Brunmeir, C. Rippinger, N. Weibrecht, C. Urach, M. Bicher, G. Schneckenreither, A. Rauber","doi":"10.1101/2020.11.07.20227462","DOIUrl":null,"url":null,"abstract":"We generate synthetic data documenting COVID-19 cases in Austria by the means of an agent-based simulation model. The model simulates the transmission of the SARS-CoV-2 virus in a statistical replica of the population and reproduces typical patient pathways on an individual basis while simultaneously integrating historical data on the implementation and expiration of population-wide countermeasures. The resulting data semantically and statistically aligns with an official epidemiological case reporting data set and provides an easily accessible, consistent and augmented alternative. Our synthetic data set provides additional insight into the spread of the epidemic by synthesizing information that cannot be recorded in reality.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation\",\"authors\":\"N. Popper, M. Zechmeister, D. Brunmeir, C. Rippinger, N. Weibrecht, C. Urach, M. Bicher, G. Schneckenreither, A. Rauber\",\"doi\":\"10.1101/2020.11.07.20227462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We generate synthetic data documenting COVID-19 cases in Austria by the means of an agent-based simulation model. The model simulates the transmission of the SARS-CoV-2 virus in a statistical replica of the population and reproduces typical patient pathways on an individual basis while simultaneously integrating historical data on the implementation and expiration of population-wide countermeasures. The resulting data semantically and statistically aligns with an official epidemiological case reporting data set and provides an easily accessible, consistent and augmented alternative. Our synthetic data set provides additional insight into the spread of the epidemic by synthesizing information that cannot be recorded in reality.\",\"PeriodicalId\":35375,\"journal\":{\"name\":\"Data Science Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Science Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2020.11.07.20227462\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2020.11.07.20227462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}

引用次数: 12

摘要

我们通过基于主体的模拟模型生成了记录奥地利COVID-19病例的合成数据。该模型模拟了SARS-CoV-2病毒在人口统计副本中的传播，并以个体为基础再现了典型的患者路径，同时整合了有关全人口对策实施和到期的历史数据。由此产生的数据在语义和统计上与官方流行病学病例报告数据集一致，并提供易于获取、一致和增强的替代方案。我们的综合数据集通过综合现实中无法记录的信息，进一步了解了这一流行病的传播情况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation

We generate synthetic data documenting COVID-19 cases in Austria by the means of an agent-based simulation model. The model simulates the transmission of the SARS-CoV-2 virus in a statistical replica of the population and reproduces typical patient pathways on an individual basis while simultaneously integrating historical data on the implementation and expiration of population-wide countermeasures. The resulting data semantically and statistically aligns with an official epidemiological case reporting data set and provides an easily accessible, consistent and augmented alternative. Our synthetic data set provides additional insight into the spread of the epidemic by synthesizing information that cannot be recorded in reality.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Data Science Journal Computer Science-Computer Science (miscellaneous)

CiteScore

5.40

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology. Details can be found in the prospectus. The scope of the journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agriculture and the Medical Science. The journal publishes papers about data and data systems; it does not publish data or data compilations. However it may publish papers about methods of data compilation or analysis.