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Research Thrust 3

Predictive Microbiome Monitoring

Projects in Research Thrust 3 (RT3), the “Modeling” thrust, develop predictive models that incorporate spatiotemporal methods, generative modeling concepts,  and machine learning approaches to analyze built environment microbiomes.

RT3 projects will focus on the development of predictive models that identify factors that contribute to microbiome compositional variations, and microbiome signatures that associate with specific health outcomes, which in turn will inform built environment health signature identification.

Sinks are ubiquitous in modern buildings and serve an important role in reducing the transmission of diseases via hand washing. However, sinks are also a known source of bioaerosol emissions. The project optimizes and standardizes methods for the study of bioaerosols for the PreMiEr ERC, tests how conditions specific to the sink (in combination with mixed microbial communities and model organisms) alter the site, size, and distribution of generated bioaerosols, and use these data to model the generation, fate, and transport of sink originating bioaerosols

Compared to the human microbiome, the microbiome of the built environment has received little attention and there remains a need for model development. Different approaches in this project seek to develop statistical models and tools that are capable of accurately characterizing built-environment microbiome compositions and link them to health outcomes, and to use advanced modeling techniques to predict and control pathogen transmission. This project also hopes to develop an understanding of the heterogeneity of the indoor microbiome and test the applicability of existing microbiome models as a basis for indoor microbiome models. Understanding such data variability and structure will aid design of future perturbation or intervention field studies.