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Markov Decision Process Approach To Strategize National Breast Cancer Screening Policy In Data-Limited Settings, Vijeta Deshpande
Markov Decision Process Approach To Strategize National Breast Cancer Screening Policy In Data-Limited Settings, Vijeta Deshpande
Masters Theses
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 …
A Computational Simulation Model For Predicting Infectious Disease Spread Using The Evolving Contact Network Algorithm, Buyannemekh Munkhbat
A Computational Simulation Model For Predicting Infectious Disease Spread Using The Evolving Contact Network Algorithm, Buyannemekh Munkhbat
Masters Theses
Commonly used simulation models for predicting outbreaks of re-emerging infectious diseases (EIDs) take an individual-level or a population-level approach to modeling contact dynamics. These approaches are a trade-off between the ability to incorporate individual-level dynamics and computational efficiency. Agent-based network models (ABNM) use an individual-level approach by simulating the entire population and its contact structure, which increases the ability of adding detailed individual-level characteristics. However, as this method is computationally expensive, ABNMs use scaled-down versions of the full population, which are unsuitable for low prevalence diseases as the number of infected cases would become negligible during scaling-down. Compartmental models use …