Abstract
The importance of analyzing data on public health, especially on notifications of disease cases, can play an important role in the overall improvement of the framework for combating epidemics. This type of study proved to be extremely influential during the COVID-19 pandemic, which demanded rapid and specific actions for the management of different geographic regions. This work presents the design and results of a system that has made possible perform analysis of this nature even in contexts of scarcity of structured and even georeferenced data. It is hoped that the work will be an inspiration for new, more comprehensive initiatives, without implying that health professionals have an advanced knowledge about the techniques and technologies that form the basis of the platform, easily integrating with existing processes and adding value. The result was the generation of a structured and aggregated database of COVID-19 data in the city of Brasilia, Federal District, capital of Brazil. If the informs used to extract data previously provided only information about the disease situation, with the platform, they now provide a basis for complex epidemiological analyzes and even spreading animations.
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Acknowledgment
This work was supported in part by CNPq - Brazilian National Research Council, under Grant 312180/2019-5 PQ-2, Grant BRICS2017-591 LargEWiN, and Grant 465741/2014-2 INCT on Cybersecurity, in part by CAPES - Brazilian Higher Education Personnel Improvement Coordination, under Grant 23038.007604/2014-69 FORTE and Grant 88887.144009/2017-00 PROBRAL, in part by FAP-DF - Brazilian Federal District Research Support Foundation, under Grant 0193.001366/2016 UIoT and Grant 0193.001365/2016 SSDDC, in part by the Brazilian Ministry of the Economy under Grant 005/2016 DIPLA and Grant 083/2016 ENAP, in part by the Institutional Security Office of the Presidency of Brazil under Grant ABIN 002/2017, in part by the Administrative Council for Economic Defense under Grant CADE 08700.000047/2019-14, and in part by the General Attorney of the Union under Grant AGU 697.935/2019.
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de Almeida, L.C., do Prado, D.S., Marques, N.A., de Caldas Filho, F.L., e Martins, L.M.C., de Sousa, R.T. (2021). Data Science Procedures to Aggregate Unstructured Disease Data in Georeferenced Spreading Analysis. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1367. Springer, Cham. https://doi.org/10.1007/978-3-030-72660-7_61
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DOI: https://doi.org/10.1007/978-3-030-72660-7_61
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