Table 3.

Summary points.

Points already known:

The identification of spatiotemporal patterns of public healthcare records is important to support changes in public policies that improve the quality of healthcare systems such as the Brazilian SUS;

There is no publicly available system to analyze COVID-19 healthcare records of the Brazilian SUS;

There is a need to understand the patterns that describe how COVID-19 affects different regions of Brazil and how such patterns change throughout time.

Contributions:

QDSSUS, a publicly available web-based visual analytics prototype built upon a customized data structure that stores millions of records and supports interactive queries that allow interactive exploration of healthcare records;

Geographical exploration of millions of Brazilian SUS healthcare records related to COVID-19 organized by admission records and patient symptoms or conditions, with support to interactive filtering of different patient demographics;

The authors discovered evolution spatiotemporal patterns in different locations of Brazil along one year of COVID-19, such as the relation of patient age groups and their corresponding dominant conditions or symptoms.