Back to results

Massachusetts Institute of Technology

Modeling and responding to pandemic influenza : importance of population distributional attributes and non-pharmaceutical interventions

Abstract

dc:description.abstract

After reviewing prevalent approaches to the modeling pandemic influenza transmission, we present a simple distributional model that captures the most significant population attributes that alter the dynamics of the outbreak. We describe how diversities in activity, susceptibility and infectivity can drive or dampen the spread of infection. We expand the model to show infection spread between several linked heterogeneous communities; this multi-community model is based on analytical calculations and Monte Carlo simulations. Focusing on mitigation strategies for a global pandemic influenza, we use our mathematical models to evaluate the implementation and timing of non-pharmaceutical intervention strategies such as travel restrictions, social distancing and improved hygiene. In addition, as we witnessed with the SARS outbreak in 2003, human behavior is likely to change during the course of a pandemic. We propose several different novel approaches to incorporating reactive social distancing and hygiene improvement and its impact on the epidemic curve. Our results indicate that while a flu pandemic could be devastating; there are non-pharmaceutical coping methods that when implemented quickly and correctly can significantly mitigate the severity of a global outbreak. We conclude with a discussion of the implications of the modeling work in the context of university planning for a pandemic.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Operations Research Center.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2009

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Nigmatulina, Karima Robert
Advisor dc:contributor.advisor
  • Richard C. Larson.

Subjects

dc:subject × 1

Rights

dc:rights
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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/53298
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/53298

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Nigmatulina, Karima Robert. Modeling and responding to pandemic influenza : importance of population distributional attributes and non-pharmaceutical interventions. Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53298