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Interview: ‘Our models could be used for many different infections, including the coronavirus’

This is an interview with a PhD student of the Complex Systems & Metagenomics projects in a series of background articles. Keep following this website for the next interview in this series.


Interview with Bastiaan van der Roest, PhD student of the project Development of Bayesian Inference Methods for Estimating Transmission Trees for Antibiotic Resistant Pathogens in Hospitals and Farms project at UMC Utrecht.

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.

‘This PhD project is an ideal combination for me. I have a background in mathematics and, as a bio-informatician, I have worked on DNA and genetics. In addition, I have always found it intriguing to model infections and pandemics. Now I am combining these models with genetics.

The models that I am working on are mainly transmission trees. By which path did an infection spread from one human to another, or from one animal to another? Based on data from hospitals, farms and other places, we are looking at the transmission of antibiotic-resistant pathogens. My aim is to make the models as realistic as possible, for instance by including the possibility of multiple introductions of an infection. These models can potentially be used for a lot of different infections, including coronavirus.

My work changed quite suddenly when this virus broke out. Together with my colleagues, I constructed models for UMC Utrecht that predicted the risk of corona infection between health care workers. This was an unexpected interruption to my PhD project, but it was also very interesting.

Hopefully, my research will eventually lead to realistic models that can be used by policymakers to prevent or extinguish infections. In recent months, we have all seen how important this is.’

PhD project: Development of Bayesian Inference Methods for Estimating Transmission Trees for Antibiotic Resistant Pathogens in Hospitals and Farms.