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ORCID

Document Type

Open Access Thesis

Degree Program

Industrial Engineering & Operations Research

Degree Type

Master of Science in Industrial Engineering and Operations Research (M.S.I.E.O.R.)

Year Degree Awarded

2019

Month Degree Awarded

September

Abstract

Early diagnosis is a promising strategy to reduce premature mortalities and for optimal use of resources. But the absence of mathematical models specific to the data settings in LMIC’s impedes the construction of economic analysis necessary for decision-makers in the development of cancer control programs. This thesis presents a new methodology for parameterizing the natural history model of breast cancer based on data availabilities in low and middle income countries, and formulation of a control optimization problem to find the optimal screening schedule for mammography screening, solved using dynamic programming. As harms and benefits are known to increase with the increase in the number of lifetime screens, the trade-off was modeled by formulating the immediate reward as a function of false positives and life-years saved. The method presented in thesis will provide optimal screening schedules for multiple scenarios of Willingness to Pay (numeric value assigned for each life-year lived), including the resulting total number of lifetime screens per person, which can help decision-makers evaluate current resource availabilities or plan future resource needs for implementation.

First Advisor

Dr. Chaitra Gopalappa

Second Advisor

Dr. Hari Balasubramanian

Third Advisor

Dr. Ana Muriel

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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