Simulating and visualizing infection spread dynamics with temporal networks

  • Jean R. Ponciano Universidade Federal de Uberlândia (UFU) / Fundação Getúlio Vargas (FGV)
  • Gabriel P. Vezono Universidade Federal de Uberlândia (UFU)
  • Claudio D. G. Linhares Universidade de São Paulo (USP)


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


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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: