EuroPOND (2016–2020) was an international research team bridging Computer Science and Neurology,
led by University College London.
Funding: EU Horizon 2020, 2016--2020, inclusive
EuroPOND website
This website is a placeholder until all our old website content can be migrated to the new hosting service. (Any volunteers? Contact Neil.)
EuroPOND Software Toolbox
We developed a unified modelling framework for neurological disease progression. Check out the toolbox here.
POND workshops/conferences
We ran biennial international workshops during EuroPOND (and continue to do so): see pondmodels.net.
The final EuroPOND workshop was CompAge2020. Driven online due to the global CoViD-19 pandemic, CompAge was a two-day virtual conference on 2 and 3 September 2020, that was originally planned to happen at the ICM Centre for Neuroinformatics in Paris, France.
For on-demand viewing of available content, go here. CompAge iss linked with a dedicated research topic in Frontiers in Artificial Intelligence – Medicine and public health (special issue).
CompAge 2020: aims and scope
Ageing is a complex phenomenon that remains poorly understood and raises great challenges for science, medicine, and society. Age-related diseases, such as neurodegenerative diseases, remain largely uncured, with attrition rates in clinical trials reaching unprecedented levels. There is no consensus on prevention measures due to limited understanding of the complex interplay between the multiple manifestations of ageing across scales, systems and organs. The spectrum of possible trajectories of healthy and pathological ageing is extraordinarily entangled, multifactorial, and heterogeneous.
Describing, modelling, and predicting the progression of slowly evolving biological processes requires the development of specific computational and data-driven methods at the cross-roads of biostatistics, machine learning, mathematical modelling, knowledge modeling and numerical simulation.
CompAge 2020 aimed to be a first-of-its-kind forum to communicate recent methodological advances in this field, and foster interactions among researchers from academia, pharmaceutical and technology industries, clinical research, and public health sectors.
Topics of interest included, but were not limited to:
- Methods to describe, classify and represent the heterogeneity of individual trajectories of ageing from multimodal and longitudinal data sets, with the aim for instance, to understand how genetic, life-style or environmental factors affect ageing;
- Dynamical models of disease progression integrating various clinical or preclinical data across scales and organs, including omics, cellular and medical imaging, physiological, cognitive, behavioral, and clinical assessments in health or disease, in humans or animal models;
- Mechanistic models of disease progression, such as pathogen spreading models in neurodegenerative diseases;
- Development of data-driven tools for precision medicine including personalised prediction of risks, prediction of future adverse events, and recommendations of personalised prevention or therapeutic strategies;
- Development of novel strategies for the identification, screening and stratification of at-risk population, including the use of digital devices or sensor data;
- Development of new clinical trial design to assess the efficacy of disease modifying agents in progressive diseases, including methods for simulation of treatment effects on ageing or disease progression;
- Methods for the analysis of epidemiological or real world data sets with the aim to identify risk factors, or assess long-term efficacy of public health policies.
We encouraged contributions from early career researchers from academia and industry.
