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PLoS ONE. Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure
[Source: PLoS ONE, full text: (LINK). Abstract, edited.]
Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure
Jeremy Hadidjojo, Siew Ann Cheong
Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Republic of Singapore
Abstract
Controlling severe outbreaks remains the most important problem in infectious disease area. With time, this problem will only become more severe as population density in urban centers grows. Social interactions play a very important role in determining how infectious diseases spread, and organization of people along social lines gives rise to non-spatial networks in which the infections spread. Infection networks are different for diseases with different transmission modes, but are likely to be identical or highly similar for diseases that spread the same way. Hence, infection networks estimated from common infections can be useful to contain epidemics of a more severe disease with the same transmission mode. Here we present a proof-of-concept study demonstrating the effectiveness of epidemic mitigation based on such estimated infection networks. We first generate artificial social networks of different sizes and average degrees, but with roughly the same clustering characteristic. We then start SIR epidemics on these networks, censor the simulated incidences, and use them to reconstruct the infection network. We then efficiently fragment the estimated network by removing the smallest number of nodes identified by a graph partitioning algorithm. Finally, we demonstrate the effectiveness of this targeted strategy, by comparing it against traditional untargeted strategies, in slowing down and reducing the size of advancing epidemics.
Citation: Hadidjojo J, Cheong SA (2011) Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure. PLoS ONE 6(7): e22124. doi:10.1371/journal.pone.0022124
Editor: Michael B. Fessler, National Institute of Environmental Health Sciences, United States of America
Received: April 15, 2011; Accepted: June 15, 2011; Published: July 22, 2011
Copyright: ? 2011 Hadidjojo, Cheong. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research is supported by Nanyang Technological University start-up grant SUG 19/07 and the Undergraduate Research Experience on Campus (URECA) programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
* E-mail: cheongsa@ntu.edu.sg.
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Jeremy Hadidjojo, Siew Ann Cheong
Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Republic of Singapore
Abstract
Controlling severe outbreaks remains the most important problem in infectious disease area. With time, this problem will only become more severe as population density in urban centers grows. Social interactions play a very important role in determining how infectious diseases spread, and organization of people along social lines gives rise to non-spatial networks in which the infections spread. Infection networks are different for diseases with different transmission modes, but are likely to be identical or highly similar for diseases that spread the same way. Hence, infection networks estimated from common infections can be useful to contain epidemics of a more severe disease with the same transmission mode. Here we present a proof-of-concept study demonstrating the effectiveness of epidemic mitigation based on such estimated infection networks. We first generate artificial social networks of different sizes and average degrees, but with roughly the same clustering characteristic. We then start SIR epidemics on these networks, censor the simulated incidences, and use them to reconstruct the infection network. We then efficiently fragment the estimated network by removing the smallest number of nodes identified by a graph partitioning algorithm. Finally, we demonstrate the effectiveness of this targeted strategy, by comparing it against traditional untargeted strategies, in slowing down and reducing the size of advancing epidemics.
Citation: Hadidjojo J, Cheong SA (2011) Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure. PLoS ONE 6(7): e22124. doi:10.1371/journal.pone.0022124
Editor: Michael B. Fessler, National Institute of Environmental Health Sciences, United States of America
Received: April 15, 2011; Accepted: June 15, 2011; Published: July 22, 2011
Copyright: ? 2011 Hadidjojo, Cheong. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research is supported by Nanyang Technological University start-up grant SUG 19/07 and the Undergraduate Research Experience on Campus (URECA) programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
* E-mail: cheongsa@ntu.edu.sg.