Estimating network structure and propagation dynamics for an infectious disease : towards effective vaccine allocation

In the event of a pandemic influenza outbreak, such as the 2009-2010 H1N1 "Swine Flu" episode, it is crucial to effectively allocate limited resources in order to minimize the casualties. Design of effective resource allocation strategies requires good understanding of the underlying contact network and of the propagation dynamics. In this thesis we develop a parameter estimation method that learns the network structure, among a family of graphs, and disease dynamics from the recorded infection curve, assuming that the disease dynamics follow an SIR process. We apply the method to data collected during the 2009-2010 H1N1 epidemic and show that the best-fit model, among a scale-free network and a small-world network, indicates the scale-free network. Given the knowledge of the network structure we evaluate different vaccination strategies. As a benchmark, we allow the vaccination decisions to depend on the state of the epidemic and we show that random vaccination (which is the current practice), does not efficiently halt the spread of influenza. Instead, we propose vaccine allocation strategies that exploit the underlying network structure and provide a reduction in the number of infections by over 6 times compared to the current practice. In addition, more realistic scenario involves random encounters between agents. To test this hypothesis, we introduced a dynamic network formation on top of the static network model. We apply the estimation method to the dynamic network model and show a small improvement in estimating the infection dynamics of the 2009-2010 H1N1 influenza.

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Kim, Louis Y. (Louis Yongchul)

Advisor dc:contributor.advisor

• Mark Abramson and Asuman Ozdaglar.

• Operations Research Center.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/91397

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/91397