EXPLORING ALTERNATIVE TREATMENT FOR BACTERIAL MENINGITIS THROUGH OPTIMAL DOSING STRATEGY: RESPONDING TO RISING ANTIBIOTIC RESISTANCE

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.