Simulating and visualizing infection spread dynamics with temporal networks

Abstract

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.