Efficient Parameter Estimation for Models of Health Care--Associated Infection in Continuous Time with Bayesian Networks

• Activity Number: 187
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Andrew Reed*+ and Alun Thomas and Karim Khader and Molly Leecaster and Tom Greene and Matthew Samore
• Companies: University of Utah and University of Utah and University of Utah and University of Utah and University of Utah and University of Utah
• Keywords: Bayesian Networks ; Susceptible & Infected models ; Markov Chain Monte Carlo integration

Abstract

We describe two novel Markov chain Monte Carlo approaches to computing estimates of parameters associated with hospital acquired infections. The first approach frames the discrete time, patient level, hospital transmission model as a Bayesian network, and exploits this framework to improve greatly on the computational efficiency of estimation compared with existing programs. The second approach is in continuous time and shares the same computational advantages. Both methods have been implemented in programs that are available from the authors. We use these programs to show that time discretization can lead to statistical bias in the underestimation of the rate of transmission of infections. We show that the continuous implementation has similar running time to the discrete, has better Markov chain mixing properties, and eliminates the potential statistical bias. We, therefore, recommend its use when continuous time data is available.