Here at CoLabo we study the epidemiology of infectious diseases by applying novel mathematical modeling and computer simulation approaches. We are interested in aggregating heterogeneous data sources, including contact tracing data, clinical symptoms, and population-level variables such as case counts, in order to make riks predictions. We are also developing participatory simulation tools that allow spreading a virual pathogen using smartphones' proximity sensing capabilites and generating synthetic epidemiological datasets to be be used for model validation. These tools have applications in STEM education as well, by supporting experiential outbreak exercises in various educational settings.

The general aim of this project is to create new machine learning models the can be used across different infectious diseases to predict diagnosis, prognosis, and forecasts, using a combination of data sources, including clinical symptoms, contact networks, and genome sequences. We are currently working on hemorrhagic viral diseases such as Ebola and Lassa fever that particularly affect low income regions in the world where access to clinical care is limited. Therefore, we are interested in translational approaches that could make these models useful through low cost technologies such as mobile phones and rapid diagnostic testing.

At CoLabo, we are looking forward to engage with the wider community through projects that have applications in STEM education and also through experimentation at the interface between science and art. Research in bioinformatics has a growing social impact as computational study of biomedical data leads to novel treatment of diseases and other advancements. Thus, we’ll aim at reaching out to the community to facilitate conversations and interest in STEM topics.

Availability of biomedical datasets of increasing size and complexity gives opportunities to create new visualization tools to gain insight from these datasets. With my work with Processing I wanted to bring science into art - that experience motivated me to now bring art into science by creating new scientific visualization tools and to be able to find possible answers to the question: what are the “next generation” visualization methods for biomedical data?