Statistical and Predictive Analysis to Identify Risk Factors and Effects of Post COVID-19 Syndrome

Based on recent studies, some COVID-19 symptoms can persist for months after infection, leading to what is termed long COVID. Factors such as vaccination timing, patient characteristics, and symptoms during the acute phase of infection may contribute to the prolonged effects and intensity of long COVID. Each patient, based on their unique combination of factors, develops a specific risk or intensity of long COVID. In this work, we aim to achieve two objectives: (1) conduct a statistical analysis to identify relationships between various factors and long COVID, and (2) perform predictive analysis of long COVID intensity using these factors. We benchmark and interpret various data-driven approaches, including linear models, random forests, gradient boosting, and neural networks, using data from the Lifelines COVID-19 cohort. Our results show that Neural Networks (NN) achieve the best performance in terms of MAPE, with predictions averaging 19\% error. Additionally, interpretability analysis reveals key factors such as loss of smell, headache, muscle pain, and vaccination timing as significant predictors, while chronic disease and gender are critical risk factors. These insights provide valuable guidance for understanding long COVID and developing targeted interventions.

A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures

The interaction between humans and AI in safety-critical systems presents a unique set of challenges that remain partially addressed by existing frameworks. These challenges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the necessity for robust and safe decision-making. A framework that holistically integrates human and AI capabilities while addressing these concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective systems. This paper proposes a holistic conceptual framework for critical infrastructures by adopting an interdisciplinary approach. It integrates traditionally distinct fields such as mathematics, decision theory, computer science, philosophy, psychology, and cognitive engineering and draws on specialized engineering domains, particularly energy, mobility, and aeronautics. The flexibility in its adoption is also demonstrated through its instantiation on an already existing framework.

NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design

The integration of machine learning (ML) techniques for addressing intricate physics problems is increasingly recognized as a promising avenue for expediting simulations. However, assessing ML-derived physical models poses a significant challenge for their adoption within industrial contexts. This competition is designed to promote the development of innovative ML approaches for tackling physical challenges, leveraging our recently introduced unified evaluation framework known as Learning Industrial Physical Simulations (LIPS). Building upon the preliminary edition held from November 2023 to March 2024, this iteration centers on a task fundamental to a well-established physical application: airfoil design simulation, utilizing our proposed AirfRANS dataset. The competition evaluates solutions based on various criteria encompassing ML accuracy, computational efficiency, Out-Of-Distribution performance, and adherence to physical principles. Notably, this competition represents a pioneering effort in exploring ML-driven surrogate methods aimed at optimizing the trade-off between computational efficiency and accuracy in physical simulations. Hosted on the Codabench platform, the competition offers online training and evaluation for all participating solutions.