OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19

Mahdi, Adam; Błaszczyk, Piotr; Dlotko, Pawel; Salvi, Dario; Chan, Tak-Shing; Harvey, John; Gurnari, Davide; Wu, Yue; Farhat, Ahmad; Hellmer, Niklas; Zarebski, Alexander; Hogan, Bernie; Tarassenko, Lionel

doi:10.1038/s41598-021-88481-4

Journal article 532 views 89 downloads

OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19

Adam Mahdi, Piotr Błaszczyk, Pawel Dlotko

, Dario Salvi, Tak-Shing Chan

, John Harvey

, Davide Gurnari, Yue Wu, Ahmad Farhat, Niklas Hellmer, Alexander Zarebski, Bernie Hogan, Lionel Tarassenko

Scientific Reports, Volume: 11, Issue: 1

Swansea University Authors: Pawel Dlotko , Tak-Shing Chan , John Harvey , Niklas Hellmer

PDF | Version of Record

© The Author(s) 2021. Tis article is licensed under a Creative Commons Attribution 4.0 International License
Download (1.61MB)

Check full text

DOI (Published version): 10.1038/s41598-021-88481-4

Abstract

Oxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level,...

Full description

Published in:	Scientific Reports
ISSN:	2045-2322
Published:	Springer Science and Business Media LLC 2021
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa57033
Tags:	Add Tag No Tags, Be the first to tag this record!

first_indexed	2021-06-05T16:21:52Z
last_indexed	2023-01-11T14:36:40Z
id	cronfa57033
recordtype	SURis
fullrecord	<?xml version="1.0"?><rfc1807><datestamp>2022-07-08T10:28:50.1323327</datestamp><bib-version>v2</bib-version><id>57033</id><entry>2021-06-05</entry><title>OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19</title><swanseaauthors><author><sid>403ec9c6f5967333948eabebe06a75f5</sid><ORCID>0000-0001-5352-3102</ORCID><firstname>Pawel</firstname><surname>Dlotko</surname><name>Pawel Dlotko</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>fb663ab91793a83f3f971322412227c2</sid><ORCID>0000-0002-8302-3974</ORCID><firstname>Tak-Shing</firstname><surname>Chan</surname><name>Tak-Shing Chan</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>1a837434ec48367a7ffb596d04690bfd</sid><ORCID>0000-0001-9211-0060</ORCID><firstname>John</firstname><surname>Harvey</surname><name>John Harvey</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>a4ee2ab85ef1dbcd71b56b2dab40f6bb</sid><firstname>Niklas</firstname><surname>Hellmer</surname><name>Niklas Hellmer</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-06-05</date><deptcode>SMA</deptcode><abstract>Oxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level, and for more than 50 countries at the regional level. It is curated from a variety of (wherever available) official sources. Its purpose is to facilitate the analysis of the spread of SARS-CoV-2 virus and to assess the effects of non-pharmaceutical interventions to reduce the impact of the pandemic. Our database is a freely available, daily updated tool that provides unified and granular information across geographical regions.</abstract><type>Journal Article</type><journal>Scientific Reports</journal><volume>11</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2045-2322</issnElectronic><keywords/><publishedDay>29</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-04-29</publishedDate><doi>10.1038/s41598-021-88481-4</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SMA</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>We acknowledge the contribution of a number of volunteers and people offering valuable feedback. In particular, we acknowledge the contributions of Abhishek Agarwal, Mario Rubio Chavarría and Tarun Srivastava. A.M. and L.T. are funded/supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. P.D. is supported by the Dioscuri Centre in Topological Data Analysis project financed under Dioscuri—a programme initiated by the Max Planck Society, jointly managed with the National Science Centre in Poland, and mutually funded by Polish Ministry of Science and Higher Education and German Federal Ministry of Education and Research as well as the EPSRC grant New Approaches to Data Science: Application Driven Topological Data Analysis EP/R018472/1. N.H. and T.-S.C. is supported by the EPSRC grant New Approaches to Data Science: Application Driven Topological Data Analysis EP/R018472/1. J.H. is supported by a Daphne Jackson Fellowship, sponsored by the EPSRC and Swansea University. Y.W. acknowledges Alan Turing Institute for funding this work through EPSRC grant EP/N510129/1 and EPSRC through the project EP/S2026347/1, titled “Unparameterised multi-modal data, high order signature, and the mathematics of data science”. A.E.Z. is supported by Oxford Martin School, Pandemic Genomics programme. D.S. is partially funded by the Swedish Knowledge Foundation through the Internet of Things and People research profile. B.H. is supported by the US National Institute of Health (R01 DA042711).</funders><lastEdited>2022-07-08T10:28:50.1323327</lastEdited><Created>2021-06-05T17:20:03.1843145</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Mathematics</level></path><authors><author><firstname>Adam</firstname><surname>Mahdi</surname><order>1</order></author><author><firstname>Piotr</firstname><surname>Błaszczyk</surname><order>2</order></author><author><firstname>Pawel</firstname><surname>Dlotko</surname><orcid>0000-0001-5352-3102</orcid><order>3</order></author><author><firstname>Dario</firstname><surname>Salvi</surname><order>4</order></author><author><firstname>Tak-Shing</firstname><surname>Chan</surname><orcid>0000-0002-8302-3974</orcid><order>5</order></author><author><firstname>John</firstname><surname>Harvey</surname><orcid>0000-0001-9211-0060</orcid><order>6</order></author><author><firstname>Davide</firstname><surname>Gurnari</surname><order>7</order></author><author><firstname>Yue</firstname><surname>Wu</surname><order>8</order></author><author><firstname>Ahmad</firstname><surname>Farhat</surname><order>9</order></author><author><firstname>Niklas</firstname><surname>Hellmer</surname><order>10</order></author><author><firstname>Alexander</firstname><surname>Zarebski</surname><order>11</order></author><author><firstname>Bernie</firstname><surname>Hogan</surname><order>12</order></author><author><firstname>Lionel</firstname><surname>Tarassenko</surname><order>13</order></author></authors><documents><document><filename>57033__20262__c03da468178f433b8d5981dc60a575e2.pdf</filename><originalFilename>57033.pdf</originalFilename><uploaded>2021-06-25T14:38:13.7475398</uploaded><type>Output</type><contentLength>1690295</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© The Author(s) 2021. Tis article is licensed under a Creative Commons Attribution 4.0 International License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling	2022-07-08T10:28:50.1323327 v2 57033 2021-06-05 OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19 403ec9c6f5967333948eabebe06a75f5 0000-0001-5352-3102 Pawel Dlotko Pawel Dlotko true false fb663ab91793a83f3f971322412227c2 0000-0002-8302-3974 Tak-Shing Chan Tak-Shing Chan true false 1a837434ec48367a7ffb596d04690bfd 0000-0001-9211-0060 John Harvey John Harvey true false a4ee2ab85ef1dbcd71b56b2dab40f6bb Niklas Hellmer Niklas Hellmer true false 2021-06-05 SMA Oxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level, and for more than 50 countries at the regional level. It is curated from a variety of (wherever available) official sources. Its purpose is to facilitate the analysis of the spread of SARS-CoV-2 virus and to assess the effects of non-pharmaceutical interventions to reduce the impact of the pandemic. Our database is a freely available, daily updated tool that provides unified and granular information across geographical regions. Journal Article Scientific Reports 11 1 Springer Science and Business Media LLC 2045-2322 29 4 2021 2021-04-29 10.1038/s41598-021-88481-4 COLLEGE NANME Mathematics COLLEGE CODE SMA Swansea University We acknowledge the contribution of a number of volunteers and people offering valuable feedback. In particular, we acknowledge the contributions of Abhishek Agarwal, Mario Rubio Chavarría and Tarun Srivastava. A.M. and L.T. are funded/supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. P.D. is supported by the Dioscuri Centre in Topological Data Analysis project financed under Dioscuri—a programme initiated by the Max Planck Society, jointly managed with the National Science Centre in Poland, and mutually funded by Polish Ministry of Science and Higher Education and German Federal Ministry of Education and Research as well as the EPSRC grant New Approaches to Data Science: Application Driven Topological Data Analysis EP/R018472/1. N.H. and T.-S.C. is supported by the EPSRC grant New Approaches to Data Science: Application Driven Topological Data Analysis EP/R018472/1. J.H. is supported by a Daphne Jackson Fellowship, sponsored by the EPSRC and Swansea University. Y.W. acknowledges Alan Turing Institute for funding this work through EPSRC grant EP/N510129/1 and EPSRC through the project EP/S2026347/1, titled “Unparameterised multi-modal data, high order signature, and the mathematics of data science”. A.E.Z. is supported by Oxford Martin School, Pandemic Genomics programme. D.S. is partially funded by the Swedish Knowledge Foundation through the Internet of Things and People research profile. B.H. is supported by the US National Institute of Health (R01 DA042711). 2022-07-08T10:28:50.1323327 2021-06-05T17:20:03.1843145 Faculty of Science and Engineering School of Mathematics and Computer Science - Mathematics Adam Mahdi 1 Piotr Błaszczyk 2 Pawel Dlotko 0000-0001-5352-3102 3 Dario Salvi 4 Tak-Shing Chan 0000-0002-8302-3974 5 John Harvey 0000-0001-9211-0060 6 Davide Gurnari 7 Yue Wu 8 Ahmad Farhat 9 Niklas Hellmer 10 Alexander Zarebski 11 Bernie Hogan 12 Lionel Tarassenko 13 57033__20262__c03da468178f433b8d5981dc60a575e2.pdf 57033.pdf 2021-06-25T14:38:13.7475398 Output 1690295 application/pdf Version of Record true © The Author(s) 2021. Tis article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title	OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19
spellingShingle	OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19 Pawel Dlotko Tak-Shing Chan John Harvey Niklas Hellmer
title_short	OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19
title_full	OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19
title_fullStr	OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19
title_full_unstemmed	OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19
title_sort	OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19
author_id_str_mv	403ec9c6f5967333948eabebe06a75f5 fb663ab91793a83f3f971322412227c2 1a837434ec48367a7ffb596d04690bfd a4ee2ab85ef1dbcd71b56b2dab40f6bb
author_id_fullname_str_mv	403ec9c6f5967333948eabebe06a75f5_*_Pawel Dlotko fb663ab91793a83f3f971322412227c2__Tak-Shing Chan 1a837434ec48367a7ffb596d04690bfd__John Harvey a4ee2ab85ef1dbcd71b56b2dab40f6bb_*_Niklas Hellmer
author	Pawel Dlotko Tak-Shing Chan John Harvey Niklas Hellmer
author2	Adam Mahdi Piotr Błaszczyk Pawel Dlotko Dario Salvi Tak-Shing Chan John Harvey Davide Gurnari Yue Wu Ahmad Farhat Niklas Hellmer Alexander Zarebski Bernie Hogan Lionel Tarassenko
format	Journal article
container_title	Scientific Reports
container_volume	11
container_issue	1
publishDate	2021
institution	Swansea University
issn	2045-2322
doi_str_mv	10.1038/s41598-021-88481-4
publisher	Springer Science and Business Media LLC
college_str	Faculty of Science and Engineering
hierarchytype
hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
hierarchy_parent_id	facultyofscienceandengineering
hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Mathematics and Computer Science - Mathematics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Mathematics
document_store_str	1
active_str	0
description	Oxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level, and for more than 50 countries at the regional level. It is curated from a variety of (wherever available) official sources. Its purpose is to facilitate the analysis of the spread of SARS-CoV-2 virus and to assess the effects of non-pharmaceutical interventions to reduce the impact of the pandemic. Our database is a freely available, daily updated tool that provides unified and granular information across geographical regions.
published_date	2021-04-29T04:12:27Z
_version_	1763753850431340544
score	11.016235

OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19

Similar Items