A Cautionary Tale on Using Covid-19 Data for Machine Learning

Abstract

Introduction: Good quality and real-time epidemiological COVID-19 data are paramount to fight this pandemic through statistical/machine-learning based decision-making support mechanisms.

Aims: Evaluate the resources available and used to gather COVID-19 epidemiological data by Portuguese health authorities from the onset of the pandemic until December 2020. The analysis laid on two main topics: (a) work processes at the Public Health Unit (PHU) level and (b) registry forms for epidemiological reporting and control procedures. Recommendations on requirements to overcome problems related to data integration and interoperability in order to build robust decision-making support mechanisms will also be produced.

Methods: For topic (a), we revised the Portuguese Directorate-General of Health (DGS) guidelines for data treatment. For topic (b), we analysed the forms used during first and second waves, while comparing them with DGS metadata provided to researchers.

Results: On topic (a), we detected the use of two complementary and non-interoperable systems. Further, the workflow does not seem to promote data quality and facilitates the occurrence of communication problems between health professionals. On topic (b), we found 27 deleted questions, 6 new questions, 1 displaced question, and 1 text modification between the 2 form versions.

Discussion: Both the workflow and data gathering methods are not the best suited for the generation of good quality data. They do not effectively support Public Health Professionals (PHP) nor provide the elements for posterior data analysis. The use of data by decision-making support mechanisms demands a careful planning of the data used to depict reality, and this condition is not met by the currently used forms.