Crowdsourcing for the spatialization and signaling of Covid-19 transmission predictors: an approach based on risk perception

  • Murilo Guerreiro Arouca UFBA
  • Carlos Daniel S. Cruz UFBA
  • Marcos Ennes Barreto UFBA / LSE
  • Isa Beatriz da C. Neves UFBA
  • Federico Costa UFBA
  • Hussein Khalil SLU
  • Ricardo Lustosa Brito UFBA


Popular participation in public health actions is essential for fighting Covid-19, especially in vulnerable urban communities where the lack of geographical data at fine resolution scale hinders appropriate spatial responses. This work proposes a crowdsourcing-based solution that captures georeferenced data regarding the population's perception of risk in relation to transmission predictors of Coronavirus. The proposed solution allows for mapping and sending real-time alerts regarding the presence of such transmission predictors. A validation study involving 20 people from a community in the city of Salvador revealed that the proposed solution is highly acceptable as user-centred alert tool, especially among young people.


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AROUCA, Murilo Guerreiro; CRUZ, Carlos Daniel S.; BARRETO, Marcos Ennes; DA C. NEVES, Isa Beatriz; COSTA, Federico; KHALIL, Hussein; BRITO, Ricardo Lustosa. Crowdsourcing for the spatialization and signaling of Covid-19 transmission predictors: an approach based on risk perception. In: SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 17. , 2022, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 73-80. ISSN 2326-2842. DOI: