Year: 2026
Type: article
Abstract: We address the problem of localizing the source of infection in an undirected, tree-structured network under a susceptible-infected outbreak model. The infection propagates with independent random time increments (i.e., edge-delays) between neighboring nodes, while only the infection times of a subset of nodes can be observed. We show that a reduced set of observers may be sufficient, in the statistical sense, to localize the source and characterize its identifiability via the joint Laplace transform of the observers' infection times. Using the explicit form of these transforms in terms of the edge-delay probability distributions, we propose scale-invariant estimators of the source. We evaluate their performance on synthetic trees and on a river network, demonstrating accurate localization under diverse edge-delay models.
Source: Bulletin of Mathematical Biology
Institutions: University of Colorado Boulder, University of Colorado System
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
Funder: University of Cambridge
OpenAlex ID: W7155707744