Arianna Dagliati – Tenure-Track Assistant Professor – Università di Pavia – Department of Electrical, Computer and Biomedical Engineering
Pietro Bosoni – Assistant Professor – Università di Pavia – Department of Electrical, Computer and Biomedical Engineering
BRAINTEASER adopts an integrated approach to understanding amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS), combining clinical, societal, and environmental data to shed light on the impact of personal exposure on disease progression. This project’s research line, led by the University of Pavia, aims to close a major knowledge gap by evaluating environmental risk factors and using advanced models to improve early detection and personalised care.
In this interview, Professors Arianna Dagliati and Pietro Bosoni share insights from their work on environmental modelling within the BRAINTEASER project, discussing methods, data challenges, and the future potential of their research.
What does ‘personal exposure’ mean, and how did you calculate it? How does this relate to disease progression?
Personal exposure refers to the level of environmental pollutants an individual encounters, shaped by geographic location, local air quality, weather conditions, and individual characteristics. We calculated personal exposure trajectories using a technique called topological data analysis (TDA). This method uses linear algebra and geometric features to integrate various environmental factors, identifying patterns and visualising how exposure evolves over time. By applying this multivariate approach, we aim to better understand how environmental factors might influence the progression of neurological diseases.
What environmental stressors are you focusing on in this study?
Our focus is primarily on the air pollution, using data from the European Air Quality Portal, managed by the European Environment Agency. We selected pollutants based on the World Health Organization’s air quality guidelines, including particulate matter (PM), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO). In addition, we used weather data from the European Climate Assessment & Dataset project, incorporating variables like wind speed, humidity, radiation, temperature, and precipitation.
How challenging is it to collect and use this environmental data, and what are the main difficulties you face?
Integrating environmental data is complex. One of the main challenges is aligning information from multiple sources both spatially and temporally. Ensuring that the data matches patients’ residential histories is critical for reliable results. To link each patient with daily pollution and weather data, we matched their residence to the closest monitoring stations using the inverse distance weighting (IDW) algorithm. This method assumes that nearby stations have more influence on the measured value than distant ones, helping us improve spatial accuracy and fill gaps where data may be missing.
How might your models be applied in the future, possibly in other diseases?
The methods we’ve developed are highly adaptable and can be extended to other chronic and complex conditions where environmental influences play a role. By providing visual and data-driven tools to assess personal exposure and its relationship with health outcomes, we hope to support clinicians, researchers, and public health authorities in making informed decisions and ultimately designing more personalised prevention and care strategies.
