Atlantes: Automated Health Related & COVID-19 Data Management for Use in Predictive Models

Abstract

The scientific community, having turned its interest, almost entirely, to the treatment and understanding of COVID-19, is constantly striving to collect and use data from the countless available sources. That data, however, is scattered, not designed to be combined, collected in different time periods and their volume is constantly increasing. In this paper, we present an automated methodology that collects, refines, groups and combines data for a large number of countries. Most of these data resources are directly related to COVID-19 but we also choose to include other types of variables for each country, which may be of particular interest for researchers working in understanding the COVID-19 pandemic. The presented methodology unifies critical information regarding the pandemic. It is implemented in Python, provided as a simple script that extracts data, in the form of a daily time series, in a short period of time, directly available to be incorporated for analysis. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.