Modeling reduction of pandemic influenza using pharmaceutical and non pharmaceutical interventions in a heterogeneous population

In an event of a pandemic influenza outbreak such as the great "Spanish Flu" of 1918 and the more recent 2009-2010 H1N1 "Swine Flu" scare, pharmaceutical as well as non-pharmaceutical resources are limited in availability and effectiveness. In this thesis we apply OR methods to evaluate the effectiveness of such resources and the strategies for reducing the number of infections resulting from an outbreak. In the first half of this work, we focus on epidemiological analysis of influenza modeling in a heterogeneous population. The majority of existing epidemiological literature models influenza spread in a statistically homogeneous population, but the model-based inclusion of heterogeneity by contact rate, susceptibility, and infectivity introduces significant effects on disease progression. We introduce a new discrete-time influenza outbreak model for a heterogeneous population and use it to describe the changes in a population's flu-related characteristics over time. This information allows us to evaluate the effectiveness of different vaccine targeting techniques in achieving herd immunity, that is, the point at which there is no further growth in new infections. In the second half of this work we switch to a practical application of OR methods in a pandemic situation. We evaluate the effectiveness of vaccines administered to US states during the 2009-2010 H1N1 pandemic. Since the US is geographically diverse and large, the outbreak progressed at different rates and started at different times in each individual state. We discuss dynamic, multi-regional, vaccine allocation schemes for large geographical entities that take into account the different conditions of the epidemic in each region and maximize the total effect of available vaccines. In addition, we discuss effective strategies for combining vaccines with non-pharmaceutical interventions such as hand-washing and public awareness campaigns to decrease the strain of an outbreak on the population.

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Teytelman, Anna

Advisor dc:contributor.advisor

• Richard C. Larson.

• 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/1721.1/72847

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/72847