Abstract: According to the CDC, Methicillin resistant Staphylococcus Aureus (or MRSA) is responsible for over 80,000 cases/year and 11,000 deaths/year in the US. To develop better regulatory policies and public health measures, we need to quantify the human health risk of MRSA infection. To estimate the infection risk of any pathogen, we need to know the probability of a person being infected by a given quantity of pathogens. This relationship is modeled using pathogen specific dose response models (DRMs). However, risk quantification for MRSA has been hindered by the absence of suitable DRMs. I will present a new approach to DRMs by modeling bacterial dynamics as a stochastic process. Then I will use clinical data on MRSA skin infections to fit this model. The resulting parameters can be used to predict the risk of MRSA infection. (Techniques used – Gillespie simulations on GPUs, parameter estimation for stochastic processes, global sensitivity analysis).
