A generalized framework for the estimation of edge infection probabilities

TL;DR

This paper introduces a flexible framework to estimate infection probabilities on network edges using multiple observations, applicable across various models, and employs a Particle Swarm heuristic for efficient optimization.

Contribution

It presents a general optimization-based framework for estimating edge infection probabilities, adaptable to multiple infection models and observation types.

Findings

High accuracy in diverse infection scenarios

Fast convergence with Particle Swarm heuristic

Effective handling of multiple observations

Abstract

Modeling the spread of infections on networks is a well-studied and important field of research. Most infection and diffusion models require a real value or probability on the edges of the network as an input, but this is rarely available in real-life applications. Our goal in this paper is to develop a general framework for this task. The general model works with the most widely used infection models and is able to handle an arbitrary number of observations on such processes. The model is defined as a general optimization task and a Particle Swarm heuristic is proposed to solve it. We evaluate the accuracy and speed of the proposed method on a high variety of realistic infection scenarios.