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

Resumo


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.

Referências

Arouca, M. G., Neves, I. B. C., and Brito, R. L. (2019). +Lugar: Um aplicativo gamificado destinado à saúde coletiva. Anais do Seminário Tecnologias Aplicadas a Educação e Saúde.

Bland, J. M. and Altman, D. G. (1997). Statistics notes: Cronbach's alpha. Bmj, 314(7080):572.

Courtney, C. (2020). Covid-19 and China's Health Code System. Somatosphere, April, 5.

de Freitas, C. S. and Ewerton, I. N. (2019). Networks for Cyberactivism and their Implications for Policymaking in Brazil. In Crowdsourcing: Concepts, Methodologies, Tools, and Applications, pages 1363–1378. IGI Global.

Ferreira, C. A. A. and Pena, F. G. (2020). O uso da tecnologia no combate ao Covid-19: uma pesquisa documental/the use of technology in the combat of Covid-19: a documentary research. Brazilian Journal of Development, 6(5):27315-27326.

Garg, L., Chukwu, E., Nasser, N., Chakraborty, C., and Garg, G. (2020a). Anonymity pre-serving iot-based Covid-19 and other infectious disease contact tracing model. IEEE Access, 8:159402–159414.

Garg, S., Bhatnagar, N., and Gangadharan, N. (2020b). A Case for Participatory Disease Surveillance of the Covid-19 pandemic in India. JMIR Public Health Surveill, 6(2):e18795.

Giulio, G. M. D., Vasconcellos, M. d. P., Günther, W. M. R., Ribeiro, H., and Assunção, J. V. d. (2015). Percepção de risco: um campo de interesse para a interface ambiente, saúde e sustentabilidade. Saúde e Sociedade, 24:1217-1231.

Huynh, T. L. et al. (2020). The Covid-19 risk perception: A survey on socioeconomics and media attention. Econ. Bull, 40(1):758–764.

Leung, G. M. and Leung, K. (2020). Crowdsourcing data to mitigate epidemics. The Lancet Digital Health, 2(4):e156-e157.

Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, pages 425-478.

Wolf, L. J., Haddock, G., Manstead, A. S., and Maio, G. R. (2020). The importance of (shared) human values for containing the Covid-19 pandemic. British Journal of Social Psychology, 59(3):618–627.
Publicado
25/04/2022
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: https://doi.org/10.5753/sbsc.2022.19477.