Abstract

Abstract
Non-pharmaceutical interventions (NPIs), such as lockdowns, travel restrictions, and social distancing mandates, play a critical role in controlling the spread of infectious diseases by shaping human mobility patterns. Using COVID-19 as a case study, this research investigates the relationships between NPIs, mobility, and the effective reproduction number (R-t) across 13 European countries. We employ XGBoost regression models to estimate missing mobility data from NPIs and missing R(t )values from mobility, achieving high accuracy. Additionally, using clustering techniques, we uncover national distinctions in social compliance. Northern European countries demonstrate higher adherence to NPIs than Southern Europe, which exhibits more variability in response to restrictions. These differences highlight the influence of cultural and social norms on public health outcomes. In general, our analysis reveals a strong correlation between NPIs and mobility reductions, highlighting the immediate impact of restrictions on population movement. However, the relationship between mobility and R(t )is weaker and more nuanced, reflecting the time delays involved, as changes in mobility take time to influence transmission rates. These results underscore the interdependence of restrictions, mobility, and disease spread while demonstrating the potential for data-driven approaches to guide policy decisions. Our approach offers valuable insights for optimizing public health strategies and tailoring interventions to diverse cultural contexts during future health crises.

Abstract

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.

Abstract

Abstract
A Bloom Filter is a probabilistic data structure designed to check, rapidly and memory-efficiently, whether an element is present in a set. It has been vastly used in various computing areas and several variants, allowing deletions, dynamic sets and working with sliding windows, have surfaced over the years. When summarizing data streams, it becomes relevant to identify the more recent elements in the stream. However, most of the sliding window schemes consider the most recent items of a data stream without considering time as a factor. While this allows, e.g., storing the most recent 10000 elements, it does not easily translate into storing elements received in the last 60 seconds, unless the insertion rate is stable and known in advance. In this paper, we present the Time-limited Bloom Filter, a new BF-based approach that can save information of a given time period and correctly identify it as present when queried, while also being able to retire data when it becomes stale. The approach supports variable insertion rates while striving to keep a target false positive rate. We also make available a reference implementation of the data structure as a Redis module.