Journal article 673 views 104 downloads
OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19
,
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)
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,...
| 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 |
| 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. |
|---|---|
| College: |
Faculty of Science and Engineering |
| 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). |
| Issue: |
1 |