Covid Data Analytics: Repositório de Dados Provenientes de Múltiplas Fontes sobre a Pandemia de COVID-19 no Brasil
Resumo
Este artigo apresenta a construção e publicação de um repositório de dados utilizados e desenvolvidos no âmbito do projeto Covid Data Analytics (CDA), executado pelo Departamento de Ciência da Computação da UFMG. O projeto visou monitorar aspectos referentes à situação social, econômica e epidemiológica da COVID-19 no Brasil a partir da análise de dados provenientes de fontes oficiais e não oficiais, de redes sociais online e da web em geral. A construção do repositório, contendo 18 atributos e 1086 registros, se baseou na coleta direta de dados das fontes selecionadas, os quais foram posteriormente enriquecidos e, finalmente, disponibilizados por meio de uma ferramenta de busca desenvolvida exclusivamente para eles.
Palavras-chave:
Covid-19, Dataset, Redes sociais
Referências
Aiello, A. E., Renson, A., and Zivich, P. N. (2020). Social media– and internet-based isease surveillance for public health. Annual Review of Public Health, 41(1):101– 118. PMID: 31905322.
Bastos, S. B. and Cajueiro, D. O. (2020). Modeling and forecasting the early evolution of the Covid-19 pandemic in Brazil. Scientific Reports, 10(1):1–10.
Du, J., Xu, J., Song, H., Liu, X., and Tao, C. (2017). Optimization on machine learning based approaches for sentiment analysis on hpv vaccines related tweets. Journal of biomedical semantics, 8(1):1–7.
Gomide, J., Veloso, A., Meira, W., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatio-temporal locality of twitter. In Proceedings of the 3rd International Web Science Conference, WebSci ’11, New York, NY, USA. Association for Computing Machinery.
Kang, G. J., Ewing-Nelson, S. R., Mackey, L., Schlitt, J. T., Marathe, A., Abbas, K. M., and Swarup, S. (2017). Semantic network analysis of vaccine sentiment in online social media. Vaccine, 35(29):3621–3638.
Li, C., Chen, L. J., Chen, X., Zhang, M., Pang, C. P., and Chen, H. (2020). Retrospective analysis of the possibility of predicting the covid-19 outbreak from internet searches and social media data, china, 2020. Eurosurveillance, 25(10).
Marques-Toledo, C. d. A., Degener, C. M., Vinhal, L., Coelho, G., Meira, W., Codeço, C. T., and Teixeira, M. M. (2017). Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting dengue at country and city level. PLoS neglected tropical diseases, 11(7):e0005729.
Moreira, P. V. X., Franco, R. A. S., Fonseca, R. M., Prado, A. C. T., Leal, L., Mendes, G. N., and Rezende, T. A. V. (2021). Covid Data Analytics: Repositório de Dados Provenientes de Múltiplas Fontes sobre a Pandemia de COVID-19 no Brasil https://doi.org/10.5281/zenodo.5176798. Zenodo.
Peixoto, P. S., Marcondes, D., Peixoto, C., and Oliva, S. M. (2020). Modeling future spread of infections via mobile geolocation data and population dynamics. an application to COVID-19 in Brazil. PloS one, 15(7):e0235732.
Pereira, I. G., Guerin, J. M., Silva Junior, A. G., Garcia, G. S., Piscitelli, P., Miani, A., Distante, C., and Gonçalves, L. M. G. (2020). Forecasting Covid-19 dynamics in Brazil: a data driven approach. International Journal of Environmental Research and Public Health, 17(14):5115.
Ranzani, O. T., Bastos, L. S., Gelli, J. G. M., Marchesi, J. F., Baiao, F., Hamacher, S., and Bozza, F. A. (2021). Characterisation of the first 250 000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. The Lancet Respiratory Medicine, 9(4):407–418.
Rey S. J., Arribas-Bel D., W. L. J. (2020). Geographic data science with pysal and the pydata stack.
Sultana, A., Tasnim, S., Hossain, M. M., Bhattacharya, S., and Purohit, N. (2021). Digital screen time during the covid-19 pandemic: a public health concern. F1000 Research, 10(81):81.
Veiga e Silva, L., de Andrade Abi Harb, M. D. P., Dos Santos, A. M. T. B., de Mattos Teixeira, C. A., Gomes, V. H. M., Cardoso, E. H. S., da Silva, M. S., Vijaykumar, N., Carvalho, S. V., Frances, C. R. L., et al. (2020). COVID-19 mortality underreporting in Brazil: analysis of data from government internet portals. Journal of medical Internet research, 22(8):e21413.
Bastos, S. B. and Cajueiro, D. O. (2020). Modeling and forecasting the early evolution of the Covid-19 pandemic in Brazil. Scientific Reports, 10(1):1–10.
Du, J., Xu, J., Song, H., Liu, X., and Tao, C. (2017). Optimization on machine learning based approaches for sentiment analysis on hpv vaccines related tweets. Journal of biomedical semantics, 8(1):1–7.
Gomide, J., Veloso, A., Meira, W., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatio-temporal locality of twitter. In Proceedings of the 3rd International Web Science Conference, WebSci ’11, New York, NY, USA. Association for Computing Machinery.
Kang, G. J., Ewing-Nelson, S. R., Mackey, L., Schlitt, J. T., Marathe, A., Abbas, K. M., and Swarup, S. (2017). Semantic network analysis of vaccine sentiment in online social media. Vaccine, 35(29):3621–3638.
Li, C., Chen, L. J., Chen, X., Zhang, M., Pang, C. P., and Chen, H. (2020). Retrospective analysis of the possibility of predicting the covid-19 outbreak from internet searches and social media data, china, 2020. Eurosurveillance, 25(10).
Marques-Toledo, C. d. A., Degener, C. M., Vinhal, L., Coelho, G., Meira, W., Codeço, C. T., and Teixeira, M. M. (2017). Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting dengue at country and city level. PLoS neglected tropical diseases, 11(7):e0005729.
Moreira, P. V. X., Franco, R. A. S., Fonseca, R. M., Prado, A. C. T., Leal, L., Mendes, G. N., and Rezende, T. A. V. (2021). Covid Data Analytics: Repositório de Dados Provenientes de Múltiplas Fontes sobre a Pandemia de COVID-19 no Brasil https://doi.org/10.5281/zenodo.5176798. Zenodo.
Peixoto, P. S., Marcondes, D., Peixoto, C., and Oliva, S. M. (2020). Modeling future spread of infections via mobile geolocation data and population dynamics. an application to COVID-19 in Brazil. PloS one, 15(7):e0235732.
Pereira, I. G., Guerin, J. M., Silva Junior, A. G., Garcia, G. S., Piscitelli, P., Miani, A., Distante, C., and Gonçalves, L. M. G. (2020). Forecasting Covid-19 dynamics in Brazil: a data driven approach. International Journal of Environmental Research and Public Health, 17(14):5115.
Ranzani, O. T., Bastos, L. S., Gelli, J. G. M., Marchesi, J. F., Baiao, F., Hamacher, S., and Bozza, F. A. (2021). Characterisation of the first 250 000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. The Lancet Respiratory Medicine, 9(4):407–418.
Rey S. J., Arribas-Bel D., W. L. J. (2020). Geographic data science with pysal and the pydata stack.
Sultana, A., Tasnim, S., Hossain, M. M., Bhattacharya, S., and Purohit, N. (2021). Digital screen time during the covid-19 pandemic: a public health concern. F1000 Research, 10(81):81.
Veiga e Silva, L., de Andrade Abi Harb, M. D. P., Dos Santos, A. M. T. B., de Mattos Teixeira, C. A., Gomes, V. H. M., Cardoso, E. H. S., da Silva, M. S., Vijaykumar, N., Carvalho, S. V., Frances, C. R. L., et al. (2020). COVID-19 mortality underreporting in Brazil: analysis of data from government internet portals. Journal of medical Internet research, 22(8):e21413.
Publicado
04/10/2021
Como Citar
MOREIRA, Pedro et al.
Covid Data Analytics: Repositório de Dados Provenientes de Múltiplas Fontes sobre a Pandemia de COVID-19 no Brasil. In: DATASET SHOWCASE WORKSHOP (DSW), 3. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 107-116.
DOI: https://doi.org/10.5753/dsw.2021.17419.
