Simulating and visualizing infection spread dynamics with temporal networks

  • Jean R. Ponciano Universidade Federal de Uberlândia (UFU) / Fundação Getúlio Vargas (FGV) http://orcid.org/0000-0003-4629-3542
  • Gabriel P. Vezono Universidade Federal de Uberlândia (UFU)
  • Claudio D. G. Linhares Universidade de São Paulo (USP)

Resumo


Temporal networks comprehend a widely adopted structure to model interactions involving a domain's instances over time. In the context of infection spread, it could be used to model face-to-face contacts among susceptible and infected individuals. By considering network visualization strategies, one can easily identify who infected whom and when, the epidemics outbreak, and other relevant behaviors. As a consequence, decision making related to the spread speed and magnitude becomes faster and more reliable. This paper presents a visual analytics approach for the simulation and analysis of infection spread dynamics that considers different infection probabilities and different levels of social distancing. We performed our experiments using two real-world social networks that represent school environments and our findings support the need for a high social distancing compliance allied to the adoption of protective measures such as the use of face masks.

Palavras-chave: Visual analytics, infection spread, temporal networks

Referências

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

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.

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.

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.

Daghriri, T. and Ozmen, O. (2020). Quantifying the effects of social distancing on the spread of covid-19. Journal of Vaccines & Vaccination.

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.

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.

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.

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.

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.

Kitchovitch, S. and Liò, P. (2010). Risk perception and disease spread on social networks. Procedia Computer Science, 1(1):2345-2354. ICCS 2010.

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.

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.

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.

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.

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.

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.

Park, J. Y. (2020). Spatial visualization of cluster-specific covid-19 transmission network in south korea during the early epidemic phase. MedRxiv.

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.

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.

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.

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.

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.

Tepper, J. G. and Thiébaut, D. (2017). Data visualization of agent-based simulation of an infectious spread. In INFOCOMP 2017.
Publicado
04/10/2021
PONCIANO, Jean R.; VEZONO, Gabriel P.; LINHARES, Claudio D. G.. Simulating and visualizing infection spread dynamics with temporal networks. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 37-48. ISSN 2763-8979. DOI: https://doi.org/10.5753/sbbd.2021.17864.