title: Optimal link removal for epidemic mitigation: a two-way partitioning approach.
abstract: The structure of the contact network through which a disease spreads may influence the optimal use of resources for epidemic control. In this work, we explore how to minimize the spread of infection via quarantining with limited resources. In particular, we examine which links should be removed from the contact network, given a constraint on the number of removable links, such that the number of nodes which are no longer at risk for infection is maximized. We show how this problem can be posed as a non-convex quadratically constrained quadratic program (QCQP), and we use this formulation to derive a link removal algorithm. The performance of our QCQP-based algorithm is validated on small Erdős-Renyi and small-world random graphs, and then tested on larger, more realistic networks, including a real-world network of injection drug use. We show that our approach achieves near optimal performance and out-performs other intuitive link removal algorithms, such as removing links in order of edge centrality.
authors: Department of Electrical Engineering, Stanford University, 117 Encina Commons, Stanford, CA 94035-6019, USA. evaenns@stanford.edu
authors: Eva A
authors: EA
authors: Enns
authors: Jeffrey J
authors: JJ
authors: Mounzer
authors: Margaret L
authors: ML
authors: Brandeau
mesh_terms: D000465:Algorithms; D003141:Communicable Diseases / Q000453:epidemiology*; D058872:Epidemics / Q000517:prevention & control*; D006801:Humans; D008962:Models, Theoretical; D011790:Quarantine / Q000592:standards*