Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic
© 2023. The Author(s)..
An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Scientific data - 10(2023), 1 vom: 07. Juni, Seite 367 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Badr, Hamada S [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 09.06.2023 Date Revised 08.11.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41597-023-02276-y |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM357885333 |
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| 520 | |a © 2023. The Author(s). | ||
| 520 | |a An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics | ||
| 650 | 4 | |a Dataset | |
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| 700 | 1 | |a Zaitchik, Benjamin F |e verfasserin |4 aut | |
| 700 | 1 | |a Kerr, Gaige H |e verfasserin |4 aut | |
| 700 | 1 | |a Nguyen, Nhat-Lan H |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yen-Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Hinson, Patrick |e verfasserin |4 aut | |
| 700 | 1 | |a Colston, Josh M |e verfasserin |4 aut | |
| 700 | 1 | |a Kosek, Margaret N |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Ensheng |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Hongru |e verfasserin |4 aut | |
| 700 | 1 | |a Marshall, Maximilian |e verfasserin |4 aut | |
| 700 | 1 | |a Nixon, Kristen |e verfasserin |4 aut | |
| 700 | 1 | |a Mohegh, Arash |e verfasserin |4 aut | |
| 700 | 1 | |a Goldberg, Daniel L |e verfasserin |4 aut | |
| 700 | 1 | |a Anenberg, Susan C |e verfasserin |4 aut | |
| 700 | 1 | |a Gardner, Lauren M |e verfasserin |4 aut | |
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