TY - GEN
T1 - A method for reducing the severity of epidemics by allocating vaccines according to centrality
AU - Drewniak, Krzysztof
AU - Helsing, Joseph
AU - Mikler, Armin R.
N1 - Publisher Copyright:
Copyright © 2014 ACM.
PY - 2014/9/20
Y1 - 2014/9/20
N2 - One long-standing question in epidemiological research is how best to allocate limited amounts of vaccine or similar preventative measures in order to minimize the severity of an epidemic. Much of the literature on the problem of vaccine allocation has focused on inuenza epidemics and used mathematical models of epidemic spread to determine the effectiveness of proposed methods. Our work applies com- putational models of epidemics to the problem of geographically allocating a limited number of vaccines within several Texas counties. We developed a graph-based, stochastic model for epidemics that is based on the SEIR model, and tested vaccine allocation methods based on multiple central- ity measures. This approach provides an alternative method for addressing the vaccine allocation problem, which can be combined with more conventional approaches to yield more effective epidemic suppression strategies. We found that al- location methods based on in-degree and inverse between- ness centralities tended to be the most effective at mitigating epidemics.
AB - One long-standing question in epidemiological research is how best to allocate limited amounts of vaccine or similar preventative measures in order to minimize the severity of an epidemic. Much of the literature on the problem of vaccine allocation has focused on inuenza epidemics and used mathematical models of epidemic spread to determine the effectiveness of proposed methods. Our work applies com- putational models of epidemics to the problem of geographically allocating a limited number of vaccines within several Texas counties. We developed a graph-based, stochastic model for epidemics that is based on the SEIR model, and tested vaccine allocation methods based on multiple central- ity measures. This approach provides an alternative method for addressing the vaccine allocation problem, which can be combined with more conventional approaches to yield more effective epidemic suppression strategies. We found that al- location methods based on in-degree and inverse between- ness centralities tended to be the most effective at mitigating epidemics.
KW - Centrality measures
KW - Computational epidemiology
KW - Health informatics
KW - Vaccine distribution
UR - http://www.scopus.com/inward/record.url?scp=84920728222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84920728222&partnerID=8YFLogxK
U2 - 10.1145/2649387.2649409
DO - 10.1145/2649387.2649409
M3 - Conference contribution
AN - SCOPUS:84920728222
T3 - ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
SP - 341
EP - 350
BT - ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
T2 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014
Y2 - 20 September 2014 through 23 September 2014
ER -