Abstract
The emergence of antibiotic resistance among nosocomial pathogens has reemphasized the need for effective infection control strategies. The spread of resistant pathogens within hospital settings proceeds along various routes of transmission and is characterized by large fluctuations in prevalence, which are typical for small populations. Identification of the most important route of colonization (exogenous by cross-transmission or endogenous caused by the selective pressure of antibiotics) is important for the design of optimal infection control strategies. Such identification can be based on a combination of epidemiological surveillance and costly and laborious as well as time-consuming methods of genotyping. Furthermore, analysis of the effects of interventions is hampered by the natural fluctuations in prevalence. To overcome these problems, we introduce a mathematical algorithm based on a Markov chain description. The input is longitudinal prevalence data only. The output is estimates of the key parameters characterizing the two colonization routes. The algorithm is tested on two longitudinal surveillance data sets of intensive care patients. The quality of the estimates is determined by comparing them to accurate estimates based on additional information obtained by genotyping. The results warrant optimism that this algorithm may help to quantify transmission dynamics and can be used to evaluate the effects of infection control interventions more carefully.