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Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic
Hamada S. Badr; Benjamin F. Zaitchik; Gaige H. Kerr; Nhat-Lan Nguyen; Yen-Ting Chen; Patrick Hinson; Josh M. Colston; Margaret N. Kosek; Ensheng Dong; Hongru Du; Maximilian Marshall; Kristen Nixon; Arash Mohegh; Daniel L. Goldberg; Susan C. Anenberg; Lauren M. Gardner.
Afiliación
  • Hamada S. Badr; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
  • Benjamin F. Zaitchik; Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD 21218
  • Gaige H. Kerr; Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC 20052
  • Nhat-Lan Nguyen; Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA 22903
  • Yen-Ting Chen; Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA 22903
  • Patrick Hinson; Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA 22903
  • Josh M. Colston; Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA 22903
  • Margaret N. Kosek; Division of Infectious Diseases and International Health, University of Virginia School of Medicine, Charlottesville, VA 22903
  • Ensheng Dong; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
  • Hongru Du; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
  • Maximilian Marshall; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
  • Kristen Nixon; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
  • Arash Mohegh; Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC 20052
  • Daniel L. Goldberg; Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC 20052
  • Susan C. Anenberg; Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC 20052
  • Lauren M. Gardner; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-21256712
ABSTRACT
An impressive number of COVID-19 data catalogs exist. None, however, are optimized for data science applications, e.g., 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 case 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, and key demographic characteristics.
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Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Preprint
Texto completo
Añadir a Mi BVS
Imprimir
XML
Buscar en Google
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Preprint