Design and Evaluation of a Data Collector and Analyzer to Monitor the COVID-19 and Other Epidemic Outbreaks

Pandemic situations require analysis, rapid decision making by managers and constant monitoring of the effectiveness of collective health-related approaches. These works can be more efficient with the help of clearer and more representative views of the data, as well as with the application of other measures and projections of epidemiological nature to the information. However, performing such aggregations of data can become a major challenge in contexts with little or no integration between databases, or even when there is no technological core mature enough to feed and integrate technological advances in the workflow of health professionals. This paper aims to present the results of the meeting of project approaches such as the OSEMN framework, a software architecture based on Microservices and Data Science technologies, all tools aligned to make the environment of descriptive and predictive analysis of epidemic data (still dominated by manual processes) evolve towards a context of automation, reliability and application of machine learning, aiming at the organization and addition of value to the results of the data structuring. The project’s validation objects were the documents of the situation of the Covid-19 disease pandemic in the region of the city of Brasilia, Federal District, Capital of Brazil.