Fractional immunization in networks

B. Aditya Prakash, Lada Adamic, Theodore Iwashyna, Hanghang Tong, Christos Faloutsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
PublisherSiam Society
Pages659-667
Number of pages9
ISBN (Print)9781611972627
StatePublished - 2013
Externally publishedYes
EventSIAM International Conference on Data Mining, SDM 2013 - Austin, United States
Duration: May 2 2013May 4 2013

Other

OtherSIAM International Conference on Data Mining, SDM 2013
CountryUnited States
CityAustin
Period5/2/135/4/13

Fingerprint

Immunization
Public health
Data transfer
Set theory

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Cite this

Prakash, B. A., Adamic, L., Iwashyna, T., Tong, H., & Faloutsos, C. (2013). Fractional immunization in networks. In Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 (pp. 659-667). Siam Society.

Fractional immunization in networks. / Prakash, B. Aditya; Adamic, Lada; Iwashyna, Theodore; Tong, Hanghang; Faloutsos, Christos.

Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society, 2013. p. 659-667.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Prakash, BA, Adamic, L, Iwashyna, T, Tong, H & Faloutsos, C 2013, Fractional immunization in networks. in Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society, pp. 659-667, SIAM International Conference on Data Mining, SDM 2013, Austin, United States, 5/2/13.
Prakash BA, Adamic L, Iwashyna T, Tong H, Faloutsos C. Fractional immunization in networks. In Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society. 2013. p. 659-667
Prakash, B. Aditya ; Adamic, Lada ; Iwashyna, Theodore ; Tong, Hanghang ; Faloutsos, Christos. / Fractional immunization in networks. Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society, 2013. pp. 659-667
@inproceedings{904307cbea0f42d584699c4c43fc870a,
title = "Fractional immunization in networks",
abstract = "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.",
author = "Prakash, {B. Aditya} and Lada Adamic and Theodore Iwashyna and Hanghang Tong and Christos Faloutsos",
year = "2013",
language = "English (US)",
isbn = "9781611972627",
pages = "659--667",
booktitle = "Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013",
publisher = "Siam Society",

}

TY - GEN

T1 - Fractional immunization in networks

AU - Prakash, B. Aditya

AU - Adamic, Lada

AU - Iwashyna, Theodore

AU - Tong, Hanghang

AU - Faloutsos, Christos

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

M3 - Conference contribution

SN - 9781611972627

SP - 659

EP - 667

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

PB - Siam Society

ER -