A Generalized Framework for the Estimation of Edge Infection Probabilities

Abstract

Modeling the spread of infections in networks is a well-studied and important field of research. Most infection and diffusion models require a real value or probability at the edges of the network as an input, but this is rarely available in real-life applications. The Generalized Inverse Infection Model (GIIM) has previously been used in real-world applications to solve this problem. However, these applications were limited to the specifics of the corresponding case studies, and the theoretical properties, as well as the wider applicability of the model, are yet to be investigated. Here, we show that the general model works with the most widely used infection models and is able to handle an arbitrary number of observations on such processes. We evaluate the accuracy and speed of the GIIM on a large variety of realistic infection scenarios.