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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp014f16c292j
Title: EXPLORING ALTERNATIVE TREATMENT FOR BACTERIAL MENINGITIS THROUGH OPTIMAL DOSING STRATEGY: RESPONDING TO RISING ANTIBIOTIC RESISTANCE
Authors: Funderburk, Kelly
Advisors: Powell, Warren
Department: Operations Research and Financial Engineering
Class Year: 2013
Abstract: The role of Markov Decision Processes in medicine is becoming more appreciated due to the flexibility of MDPs and their natural applicability to the health setting and individualized patient response. This paper utilizes a stochastic dynamic programming model to inform dosing decisions in a lesser known variant of antibiotic therapy for bacterial meningitis. The problem is incentivized by the rise of antimicrobial resistance and the resultant push for the medical community to explore therapeutic options for treating communicable disease beyond the accepted traditional antibiotic therapies. The implication is that the rise of resistance is on its way to saddling the global populace - especially within the developed world - ­the astronomic burden of pre-antibiotic era communicable diseases which are increasingly less respondent to typical antibiotics. Even vaccination is being rendered significantly less effective by the rapid development of new bacterial strains. Bacterial Meningitis, one communicable disease, is particularly affected by antibiotic resistance, because swift delivery of effective antibiotics is so key in its management. This study explores the drug linezolid through stochastic modeling and optimal dosing strategy.
Extent: 81 pages
URI: http://arks.princeton.edu/ark:/88435/dsp014f16c292j
Access Restrictions: Walk-in Access. This thesis can only be viewed on computer terminals at the Mudd Manuscript Library.
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2016

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