This is a unified COVID-19 dataset to fulfill the following objectives:
- Mapping all geospatial units globally into a unique standardized ID.
- Standardizing administrative names and codes at all levels.
- Standardizing dates, data types, and formats.
- Unifying variable names, types, and categories.
- Merging data from all credible sources at all levels.
- Cleaning the data and fixing confusing entries.
- Integrating hydrometeorological variables at all levels.
- Integrating population-weighted hydrometeorological variables.
- Integrating air quality, comorbidities, WorldPop, and other static data.
- Integrating policy data from Oxford government response tracker.
- Integrating vaccination data from JHU Centers for Civic Impact.
- Integrating estimates of daily infections (cases by date of infection).
- Integrating an augmented version from all sources (future releases).
- Generating epidemiological estimates of infections and effective reproduction number.
- Optimizing the data for machine learning applications.
- Providing multiple data formats, including the lightning fast
fst. - Providing code to efficiently load and combine/subset the datasets (coming soon).
Note that COVID-19 data for some European countries from Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE) are reported in the global daily reports at province level, which will be replaced by higher-resolution data at NUTS 0-3 levels.
| Column | Type | Description |
|---|---|---|
| ID | Character | Geospatial ID, unique identifier |
| Date | Date | Date of data record |
| Cases | Integer | Number of cumulative cases |
| Cases_New | Integer | Number of new daily cases |
| Type | Character | Type of the reported cases |
| Age | Character | Age group of the reported cases |
| Sex | Character | Sex/gender of the reported cases |
| Source | Character | Data source: JHU, CTP, NYC, NYT, UVA, SES, DPC, RKI, JRC, IHME |
| Type | Description |
|---|---|
| Active | Active cases |
| Confirmed | Confirmed cases |
| Deaths | Deaths |
| Home_Confinement | Home confinement / isolation |
| Hospitalized | Total hospitalized cases excluding intensive care units |
| Hospitalized_Now | Currently hospitalized cases excluding intensive care units |
| Hospitalized_Sym | Symptomatic hospitalized cases excluding intensive care units |
| ICU | Total cases in intensive care units |
| ICU_Now | Currently in intensive care units |
| Infections | Estimated infections |
| Negative | Negative tests |
| Pending | Pending tests |
| Positive | Positive tests, including hospitalised cases and home confinement |
| Positive_Dx | Positive cases emerged from clinical activity / diagnostics |
| Positive_Sc | Positive cases emerging from surveys and tests |
| Recovered | Recovered cases |
| Tested | Cases tested = Tests - Pending |
| Tests | Total performed tests |
| Ventilator | Total cases receiving mechanical ventilation |
| Ventilator_Now | Currently receiving mechanical ventilation |
This work is supported by NASA Health & Air Quality project 80NSSC18K0327, under a COVID-19 supplement, and National Institute of Health (NIH) project 3U19AI135995-03S1 ("Consortium for Viral Systems Biology (CViSB)"; Collaboration with The Scripps Research Institute and UCLA).
To cite this dataset:
Badr, H.S., Zaitchik, B.F., Kerr, G.H. et al. Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic. Sci Data 10, 367 (2023). https://doi.org/10.1038/s41597-023-02276-y
@article{Badr2023,
title = {Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic},
author = {Badr, Hamada S. and Zaitchik, Benjamin F. and Kerr, Gaige H. and Nguyen, Nhat-Lan H. and Chen, Yen-Ting and Hinson, Patrick and Colston, Josh M. and Kosek, Margaret N. and Dong, Ensheng and Du, Hongru and Marshall, Maximilian and Nixon, Kristen and Mohegh, Arash and Goldberg, Daniel L. and Anenberg, Susan C. and Gardner, Lauren M.},
year = 2023,
month = {Jun},
day = {07},
journal = {Scientific Data},
volume = 10,
number = 1,
pages = 367,
doi = {10.1038/s41597-023-02276-y},
issn = {2052-4463},
url = {https://doi.org/10.1038/s41597-023-02276-y},
abstract = {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 epidemio logical 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.}
}TY - JOUR
AU - Badr, Hamada S.
AU - Zaitchik, Benjamin F.
AU - Kerr, Gaige H.
AU - Nguyen, Nhat-Lan H.
AU - Chen, Yen-Ting
AU - Hinson, Patrick
AU - Colston, Josh M.
AU - Kosek, Margaret N.
AU - Dong, Ensheng
AU - Du, Hongru
AU - Marshall, Maximilian
AU - Nixon, Kristen
AU - Mohegh, Arash
AU - Goldberg, Daniel L.
AU - Anenberg, Susan C.
AU - Gardner, Lauren M.
PY - 2023
DA - 2023/06/07
TI - Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic
JO - Scientific Data
SP - 367
VL - 10
IS - 1
AB - 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 epidemio
logical 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.
SN - 2052-4463
UR - https://doi.org/10.1038/s41597-023-02276-y
DO - 10.1038/s41597-023-02276-y
ID - Badr2023
ER -