TY - GEN

T1 - Fractional immunization in networks

AU - Prakash, B. Aditya

AU - Adamic, Lada

AU - Iwashyna, Theodore

AU - Tong, Hanghang

AU - Faloutsos, Christos

N1 - Publisher Copyright:
Copyright © SIAM.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2013

Y1 - 2013

N2 - Preventing contagion in networks is an important problem in public health and other domains. Targeting nodes to immunize based on their network interactions has been shown to be far more effective at stemming infection spread than immunizing random subsets of nodes. However, the assumption that selected nodes can be rendered completely immune does not hold for infections for which there is no vaccination or effective treatment. Instead, one can confer fractional immunity to some nodes by allocating variable amounts of infection-prevention resource to them. We formulate the problem to distribute a fixed amount of resource across nodes in a network such that the infection rate is minimized, prove that it is NP-complete and derive a highly effective and efficient linear-time algorithm. We demonstrate the efficiency and accuracy of our algorithm compared to several other methods using simulation on realworld network datasets including US-MEDICARE and state-level interhospital patient transfer data. We find that concentrating resources at a small subset of nodes using our algorithm is up to 6 times more effective than distributing them uniformly (as is current practice) or using network-based heuristics. To the best of our knowledge, we are the first to formulate the problem, use truly nation-scale network data and propose effective algorithms.

AB - Preventing contagion in networks is an important problem in public health and other domains. Targeting nodes to immunize based on their network interactions has been shown to be far more effective at stemming infection spread than immunizing random subsets of nodes. However, the assumption that selected nodes can be rendered completely immune does not hold for infections for which there is no vaccination or effective treatment. Instead, one can confer fractional immunity to some nodes by allocating variable amounts of infection-prevention resource to them. We formulate the problem to distribute a fixed amount of resource across nodes in a network such that the infection rate is minimized, prove that it is NP-complete and derive a highly effective and efficient linear-time algorithm. We demonstrate the efficiency and accuracy of our algorithm compared to several other methods using simulation on realworld network datasets including US-MEDICARE and state-level interhospital patient transfer data. We find that concentrating resources at a small subset of nodes using our algorithm is up to 6 times more effective than distributing them uniformly (as is current practice) or using network-based heuristics. To the best of our knowledge, we are the first to formulate the problem, use truly nation-scale network data and propose effective algorithms.

UR - http://www.scopus.com/inward/record.url?scp=84934293884&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84934293884&partnerID=8YFLogxK

U2 - 10.1137/1.9781611972832.73

DO - 10.1137/1.9781611972832.73

M3 - Conference contribution

AN - SCOPUS:84934293884

T3 - Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

SP - 659

EP - 667

BT - Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

A2 - Ghosh, Joydeep

A2 - Obradovic, Zoran

A2 - Dy, Jennifer

A2 - Zhou, Zhi-Hua

A2 - Kamath, Chandrika

A2 - Parthasarathy, Srinivasan

PB - Siam Society

T2 - SIAM International Conference on Data Mining, SDM 2013

Y2 - 2 May 2013 through 4 May 2013

ER -