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COVIData: a Web Platform for Tracking, Classification and Monitoring Cases Suspects of COVID-19

EasyChair Preprint no. 9937

11 pagesDate: April 6, 2023


TThe high speed spread of SARS-CoV-2 trough out the entire globe has ignited the warning about the importance and need to collect data from patients possibly infected, on a massive scale, in order to understand spreading dynamics of this particular disease. Having stated that, this article aims to present the entire development of the web platform COVIData, created by students and researches of the Federal University of ABC (UFABC) while in partnership with Inter-municipal Consortium of ABC, resulting in a tool directed to people' self-screening their symptoms and being able to have and immediate identification of whether they are or not possibly infected.The tool consists of a detailed questionnaire based on scientific data on the most common symptoms of the disease. The questionnaire has been validated by healthcare professionals to verify the correlation of symptoms described by individuals using COVIData with SARS-CoV-2 infection. Furthermore, as a result, data analysis may be made and enhanced viewing the possibility to discuss, develop and implement public policies that help facing the disease.

Keyphrases: COVID-19, database, Decision Support System, health monitoring, public health, self-screening

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Beatriz Lima Gandolfi and Clarissa S. R. Merino and Vitor Inacio da Silva and Diego S. Costa and Gabriel de M. Fiali and Andre S. Carvalheiro and Luiz Rodrigo C. da Silva and Camila C. Rocha and Giovanna B. Lins and Saul de Castro Leite and Fernanda Nascimento Almeida},
  title = {COVIData: a Web Platform for Tracking, Classification and Monitoring Cases Suspects of COVID-19},
  howpublished = {EasyChair Preprint no. 9937},

  year = {EasyChair, 2023}}
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