Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic

• Main Authors: Teodoro Alamo, Daniel G. Reina, Martina Mammarella, Alberto Abella
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Electronics
• Subjects: Covid-19; coronavirus; SARS-CoV-2; open data; data-driven methods; machine learning
• Online Access: https://www.mdpi.com/2079-9292/9/5/827

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.