Open AccessSimulating and visualizing infection spread dynamics with temporal networks
Author(s) -
Jean R. Ponciano,
Gabriel P. Vezono,
Cláudio D. G. Linhares
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbbd.2021.17864
Subject(s) - social distance , computer science , context (archaeology) , visualization , visual analytics , face (sociological concept) , distancing , dynamics (music) , domain (mathematical analysis) , data science , human–computer interaction , covid-19 , artificial intelligence , psychology , geography , infectious disease (medical specialty) , medicine , mathematical analysis , social science , pedagogy , mathematics , disease , archaeology , pathology , sociology
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.