A Visualization Approach for Simulating and Analyzing Infection Spread Dynamics Using Temporal Networks

Abstract

Temporal networks have been widely used to model instances of a domain of interest and their time-evolving interaction, including modeling individuals and face-to-face contacts throughout time. In the context of infection spread, such individuals can, e.g., remain susceptible, recovered, or be infected at a particular time. Understanding the infection spread behavior (its speed and magnitude, for instance) is crucial for quick and reliable decision making. Network visualization strategies can help in this task as they allow easy identification of who infected whom and when, epidemics outbreak, and other relevant aspects. This paper presents a visualization approach for the simulation and analysis of infection spread dynamics that considers different infection probabilities and different levels of social distancing (inter-group interaction). We performed quantitative and visual experiments using three real-world social networks with distinct characteristics and from two different environments. Our findings reveal the overall influence of different levels of inter-group interaction and infection probabilities in the infection spread dynamics and also demonstrate the usefulness of our approach for enhanced local (individual- or group-level) investigations.