Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic
Título
Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic
Autor
Teodoro Alamo, Daniel G. Reina, Martina Mammarella, Alberto Abella
Descripción
We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources.
Fecha
2020
Materia
coronavirus, covid-19, SARS-CoV-2, machine learning, open data, data-driven methods
Identificador
10.3390/electronics9050827
Fuente
Epidemiology and Health
Editor
Korean Society of Epidemiology
Cobertura
Electronics
Colección
Citación
Teodoro Alamo, Daniel G. Reina, Martina Mammarella, Alberto Abella, “Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic,” SOCICT Open, consulta 19 de abril de 2026, https://www.socictopen.socict.org/items/show/7222.
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