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English Information

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Authors
# Name
1 Jean Ponciano(jean@ufu.br)
2 Gabriel Vezono(gvezono@gmail.com)
3 Claudio Linhares(claudiodgl@usp.br)

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Reference
# Reference
1 Beck, F., Burch, M., Diehl, S., and Weiskopf, D. (2016). A taxonomy and survey of dynamic graph visualization. Computer Graphics Forum, 36(1):133–159
2 Canabarro, A., Tenorio, E., Martins, R., Martins, L., Brito, S., and Chaves, R. (2020). Data-driven study of the covid-19 pandemic via age-structured modelling and prediction of the health system failure in brazil amid diverse intervention strategies. medRxiv
3 Carroll, L. N., Au, A. P., Detwiler, L. T., Fu, T.-c., Painter, I. S., and Abernethy, N. F. (2014). Visualization and analytics tools for infectious disease epidemiology: a systematic review. Journal of biomedical informatics, 51:287–298
4 Chang, S., Harding, N., Zachreson, C., Cliff, O. M., and Prokopenko, M. (2020). Modelling transmission and control of the covid-19 pandemic in australia. ArXiv, abs/2003.10218
5 Daghriri, T. and Ozmen, O. (2020). Quantifying the effects of social distancing on the spread of covid-19. Journal of Vaccines & Vaccination
6 Dong, E., Du, H., and Gardner, L. (2020). An interactive web-based dashboard to track covid-19 in real time. The Lancet infectious diseases, 20(5):533–534
7 Dunne, C., Muller, M., Perra, N., and Martino, M. (2015). Vorograph: Visualization tools for epidemic analysis. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, CHI EA ’15, page 255–258, New York, NY, USA. Association for Computing Machinery
8 Gemmetto, V., Barrat, A., and Cattuto, C. (2014). Mitigation of infectious disease at school: targeted class closure vs school closure. BMC infectious diseases, 14(1):695
9 Ghalmane, Z., El Hassouni, M., and Cherifi, H. (2019). Immunization of networks with non-overlapping community structure. Social Network Analysis and Mining, 9(1):45
10 Jr., L. G. and Frizzon, G. (2019). Fake news and brazilian politics – temporal investigation based on semantic annotations and graph analysis. In Anais do XXXIV SBBD, pages 169–174, Porto Alegre, RS, Brasil. SBC
11 Leão, J., Laender, A., and de Melo, P. (2019). A multi-strategy approach to overcoming bias in community detection evaluation. In Anais do XXXIV SBBD, pages 13–24, Porto Alegre, RS, Brasil. SBC
12 Linhares, C., Ponciano, J., Pereira, F., Rocha, L., Paiva, J., and Travençolo, B. (2020a). Visual analysis for evaluation of community detection algorithms. MTAP, 79(25):17645–17667
13 Linhares, C. D. G., Ponciano, J. R., Paiva, J. G. S., Rocha, L. E. C., and Travençolo, B. A. N. (2020b). DyNetVis - an interactive software to visualize structure and epidemics on temporal networks. In 2020 IEEE/ACM ASONAM, pages 933–936.
14 Linhares, C. D. G., Ponciano, J. R., Paiva, J. G. S., Travençolo, B. A. N., and Rocha, L. E. C. (2019a). Visualisation of Structure and Processes on Temporal Networks, pages 83–105. Springer International Publishing, Cham
15 Linhares, C. D. G., Ponciano, J. R., Pereira, F. S. F., Rocha, L. E. C., Paiva, J. G. S., and Travençolo, B. A. (2019b). A scalable node ordering strategy based on community structure for enhanced temporal network visualization. Computers & Graphics, 84:185 – 198
16 Mastrandrea, R., Fournet, J., and Barrat, A. (2015). Contact patterns in a high school: A comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLOS ONE, 10(9):1–26
17 Park, J. Y. (2020). Spatial visualization of cluster-specific covid-19 transmission network in south korea during the early epidemic phase. medRxiv
18 Ponciano, J. R., Linhares, C. D., Faria, E. R., and Travençolo, B. A. (2021a). An online and nonuniform timeslicing method for network visualisation. C&G, 97:170–182
19 Ponciano, J. R., Linhares, C. D., Melo, S. L., Lima, L. V., and Travençolo, B. A. (2020). Visual analysis of contact patterns in school environments. Informatics in Education, 19(3):455–472
20 Ponciano, J. R., Linhares, C. D. G., Rocha, L. E. C., Faria, E. R., and Travençolo, B. A. N. (2021b). A streaming edge sampling method for network visualization. KAIS, 63:1717–1743
21 Prakash, B. A., Vreeken, J., and Faloutsos, C. (2014). Efficiently spotting the starting points of an epidemic in a large graph. Knowledge and information systems, 38(1):35–59
22 So, M., Tiwari, A., Chu, A., Tsang, J., and Chan, J. (2020). Visualizing covid-19 pandemic risk through network connectedness. International Journal of Infectious Diseases, 96:558 – 561
23 Tepper, J. G. and Thiébaut, D. (2017). Data visualization of agent-based simulation of an infectious spread. In INFOCOMP 2017
24 Kitchovitch, S. and Liò, P. (2010). Risk perception and disease spread on social networks. Procedia Computer Science, 1(1):2345 – 2354. ICCS 2010.