Pitt Logo LinkContact Us

Optimal Design of the Annual Influenza Vaccine

Ozaltin, Osman / OY (2012) Optimal Design of the Annual Influenza Vaccine. Doctoral Dissertation, University of Pittsburgh.

[img]
Preview
PDF - Primary Text
Download (2369Kb) | Preview

    Abstract

    Seasonal influenza is a major public health concern, and the first line of defense is the flu shot. Antigenic drifts and the high rate of influenza transmission require annual updates to the flu shot composition. The World Health Organization recommends which flu strains to include in the annual vaccine based on surveillance and epidemiological analysis. There are two critical decisions regarding the flu shot design. One is its composition; currently, three strains constitute the flu shot, and they influence vaccine effectiveness. Another critical decision is the timing of the composition decisions, which affects the flu shot production. Both of these decisions have to be made under uncertainty many months before the flu season starts. We quantify the trade offs involved through multistage stochastic mixed-integer programs that determine the optimal flu shot composition and its timing in a stochastic and dynamic environment. Our first model takes the view of a social planner, and optimizes strain selections based on a production plan that is provided by the flu shot manufacturers. It also incorporates risk-sensitivity through mean-risk models. Our second model relaxes the exogenous production planning assumption and, hence, provides a more accurate representation of the hierarchical decision mechanism between a social planner, who selects the flu shot strains, and the manufacturers, who make the flu shot available. We derive structural properties of both models, and calibrate them using publicly available data. The flu shot strains are updated based on clinical, virological and immunological surveillance. In the virological surveillance, hemagglutinin inhibition assays are used to identify antigenic properties of the influenza viruses. However, this serology assay is labor-intensive and time-consuming. As an alternative, pairwise amino acid sequence comparison of influenza strains is used in statistical learning models to identify positions that cause antigenic variety. The performance of these models is evaluated by cross validation. In Chapter 5, we formulate cross validation as a bilevel program where an upper-level program chooses the model variables to minimize the out-of-sample error, and lower-level problems in each fold optimize in-sample errors according to their training data set by selecting the regression coefficients of the chosen model variables. We provide an extensive computational study using clinical data, and identify amino acid positions that significantly contribute to antigenic variety of influenza strains.


    Share

    Citation/Export:
    Social Networking:

    Details

    Item Type: University of Pittsburgh ETD
    Creators/Authors:
    CreatorsEmailORCID
    Ozaltin, Osman / OYoyo1@pitt.edu
    ETD Committee:
    ETD Committee TypeCommittee MemberEmailORCID
    Committee ChairSchaefer, Andrew J. / AJschaefer@engr.pitt.edu
    Committee CoChairProkopyev, Oleg A. / OAprokopyev@engr.pitt.edu
    Committee MemberVielma, Juan P. / JPjvielma@pitt.edu
    Committee MemberTrick, Michaek/ Mtrick@cmu.edu
    Title: Optimal Design of the Annual Influenza Vaccine
    Status: Published
    Abstract: Seasonal influenza is a major public health concern, and the first line of defense is the flu shot. Antigenic drifts and the high rate of influenza transmission require annual updates to the flu shot composition. The World Health Organization recommends which flu strains to include in the annual vaccine based on surveillance and epidemiological analysis. There are two critical decisions regarding the flu shot design. One is its composition; currently, three strains constitute the flu shot, and they influence vaccine effectiveness. Another critical decision is the timing of the composition decisions, which affects the flu shot production. Both of these decisions have to be made under uncertainty many months before the flu season starts. We quantify the trade offs involved through multistage stochastic mixed-integer programs that determine the optimal flu shot composition and its timing in a stochastic and dynamic environment. Our first model takes the view of a social planner, and optimizes strain selections based on a production plan that is provided by the flu shot manufacturers. It also incorporates risk-sensitivity through mean-risk models. Our second model relaxes the exogenous production planning assumption and, hence, provides a more accurate representation of the hierarchical decision mechanism between a social planner, who selects the flu shot strains, and the manufacturers, who make the flu shot available. We derive structural properties of both models, and calibrate them using publicly available data. The flu shot strains are updated based on clinical, virological and immunological surveillance. In the virological surveillance, hemagglutinin inhibition assays are used to identify antigenic properties of the influenza viruses. However, this serology assay is labor-intensive and time-consuming. As an alternative, pairwise amino acid sequence comparison of influenza strains is used in statistical learning models to identify positions that cause antigenic variety. The performance of these models is evaluated by cross validation. In Chapter 5, we formulate cross validation as a bilevel program where an upper-level program chooses the model variables to minimize the out-of-sample error, and lower-level problems in each fold optimize in-sample errors according to their training data set by selecting the regression coefficients of the chosen model variables. We provide an extensive computational study using clinical data, and identify amino acid positions that significantly contribute to antigenic variety of influenza strains.
    Date: 02 February 2012
    Date Type: Publication
    Defense Date: 29 July 2011
    Approval Date: 02 February 2012
    Submission Date: 06 November 2011
    Release Date: 02 February 2012
    Access Restriction: No restriction; The work is available for access worldwide immediately.
    Patent pending: No
    Number of Pages: 130
    Institution: University of Pittsburgh
    Thesis Type: Doctoral Dissertation
    Refereed: Yes
    Related URLs:
    Degree: PhD - Doctor of Philosophy
    Uncontrolled Keywords: Operations research, mixed-integer programming, multistage stochastic programming, bilevel programming, column generation, branch-and-price, oligopolistic market, influenza vaccine design.
    Schools and Programs: Swanson School of Engineering > Industrial Engineering
    Date Deposited: 02 Feb 2012 12:39
    Last Modified: 16 Jul 2014 17:02

    Actions (login required)

    View Item

    Document Downloads