Abstract
AbstractNon-pharmaceutical interventions (NPIs), including measures such as lockdowns, travel limitations, and social distancing mandates, play a critical role in shaping human mobility, which subsequently influences the spread of infectious diseases. Using COVID-19 as a case study, this research examines the relationship between restrictions, mobility patterns, and the disease’s effective reproduction number (Rt) across 13 European countries. Employing clustering techniques, we uncover distinct national patterns, highlighting differences in social compliance between Northern and Southern Europe. While restrictions strongly correlate with mobility reductions, the relationship between mobility and Rtis more nuanced, driven primarily by the nature of social interactions rather than mere compliance. Additionally, employing XGBoost regression models, we demonstrate that missing mobility data can be accurately inferred from restrictions, and missing infection rates can be predicted from mobility data. These findings provide valuable insights for tailoring public health strategies in future crisis and refining analytical approaches.

Abstract

Abstract

Abstract
Virtual presence demands ultra-low latency, a factor that centralized architectures, by their nature, cannot minimize. Local peer-to-peer architectures offer a compelling alternative, but also pose unique challenges in terms of network infrastructure.This paper introduces a prototype leveraging Conflict-Free Replicated Data Types (CRDTs) to enable real-time collaboration in a shared virtual environment. Using this prototype, we investigate latency, synchronization, and the challenges of decentralized coordination in dynamic non-Byzantine contexts.We aim to question prevailing assumptions about decentralized architectures and explore the practical potential of P2P in advancing virtual presence. This work challenges the constraints of mediated networks and highlights the potential of decentralized architectures to redefine collaboration and interaction in digital spaces. © 2025 Copyright is held by the owner/author(s).

Abstract
In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.