Development of Bayesian Inference Methods for Estimating Transmission Trees for Antibiotic Resistant Pathogens in Hospitals and Farms
Group and collaboration
Collaboration: Mirjam Kretzschmar (UMCU), Martin Bootsma (UMCU), Egil Fischer (UU)
PhD Student: Bastiaan Van der Roest
The spread of antibiotic resistant pathogens is an increasing problem in hospitals and livestock farming. In both settings, surveillance is performed to detect colonisation with these pathogens in order to implement intervention measures.
As genetic sequencing is becoming more readily available, the question arises to what extent information from sequence data can be used in combination with other types of data to estimate transmission routes of pathogens. In recent years, different computational methods have been developed to estimate transmission trees from genetic and epidemiological data, but none of these methods are applicable to our settings as the assumptions they are based on do not apply.
Difficulties are the inflow and outflow in populations and incomplete observations, both pertinent to a surveillance situation in hospitals and
livestock farms. In this project, we plan to extend and further develop methods to estimate transmission trees so that they can be applied to data from these settings, and possibly in the future be implemented into surveillance strategies for antibiotic resistant pathogens.
PhD student interview
In the middle of a global pandemic, Bastiaan van der Roest is working on transmission trees: pathways that show how an infection spreads. The resulting models can potentially be used by policymakers to prevent or extinguish infections.