Evaluating the impact of the weather conditions on the influenza propagation

We show that the simulation results have the same propagation shape as the weekly influenza rates asrecorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show thata diminishing relative humidity of 10% produces an increment of about 1.6% in the final infection rate. The effect oftemperature changes on the infection spread is also noticeable, with a decrease of 1.1% per extra degree.Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and wouldpermit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We makeEpiGraph source code and epidemic data publicly availableThis work has been partially supported by the Spanish “Ministerio de Economía y Competitividad” under the project grant TIN2016-79637-P “Towards Unification of HPC and Big Data paradigms”. The work of Maria-Cristina Marinescu has been partially supported by the H2020 European project GrowSmarter under project grant ref. 646456. The role of both funders was limited to financial support and did not imply participation of any kind in the study and collection, analysis, and interpretation of data, nor in the writing of the manuscrip

QUARQ: QUick approximate and relaxed querying

Executing queries over Linked Open Data (LOD) is a complex task. The total number of sources triggered by a single query cannot be known in advance, nor the reasoning complexity applied to each source. In order to avoid this uncertainty, practitioners download full replicas of the open data and build applications on top of the datasets in a controlled environment. With this centralized approach, they lose dynamic data changes, and often they cannot account for the inference capabilities defined in the associated ontologies. In this work, we explore the feasibility of predicting the performance of Flexible Querying over Linked Open Data [1]. Concretely, we propose QUARQ: QUick Approximate and Relaxed Querying, a tool that using ML provides intelligence to the process of generating alternative queries that run more efficiently than the original ones. With this tool, we propose avoiding the use of replicated Linked Data by seizing the shareable nature of Linked Data and eluding the impracticality of maintaining copies up-to-date or the need to work with outdated data

A semantic model to fight social exclusion

This work presents a semantic model meant to help with the identification and prediction of individuals at risk of social exclusion. The model is based on the self-sufficiency matrix, a tool that evaluates a person's self-sufficiency in different areas, and that is used by Barcelona's City Council. Existing data sources can then be mapped to this model, in order to analyze, query, and visualize the data.This work is partially supported by the Semiotic project, funded by Ministerio de Economia, Industria, y Competitividad (TIN2016-78473-C3-2-R).Peer ReviewedPostprint (author's final draft

Data management in epiGraph COVID-19 epidemic simulator

The transmission of COVID-19 through a population depends on many factors which model, incorporate, and integrate a large number of heterogeneous data sources. The work we describe in this paper focuses on the data management aspect of EpiGraph, a scalable agent-based virus-propagation simulator. We describe the data acquisition and pre-processing tasks that are necessary to map the data to the different models implemented in EpiGraph in a way that is efficient and comprehensible. We also report on post-processing, analysis, and visualization of the outputs, tasks that are fundamental to make the simulation results useful for the final users. Our simulator captures complex interactions between social processes, virus characteristics, travel patterns, climate, vaccination, and non-pharmaceutical interventions. We end by demonstrating the entire pipeline with one evaluation for Spain for the third COVID wave starting on December 27th of 2020.This work has been supported by the Spanish Instituto de Salud Carlos III under the project grant 2020/00183/001, the project grant BCV-2021-1-0011, of the Spanish Supercomputing Network (RES) and the European Union's Horizon 2020 JTI-EuroHPC research and innovation program under grant agreement No 956748

EpiGraph 1.3 meteorological-based experimental data

<p>This file contains the complete data set of EpiGraph version 1.3.</p> <p>EpiGraph is a scalable, fully distributed simulator that is able to perform large scale stochastic and realistic simulations of the propagation of the flu virus. The current implementation of EpiGraph allows modeling the population via a realistic local interconnection network based on actual individual interactions extracted from social networks and demographic data. At inter-urban scale, we use a transportation model which allows the study of the spatial dynamics of the virus propagation over large geographical areas. EpiGraph also includes a model of the interaction between influenza spreading and climatic and meteorological factors, such as temperature, atmospheric pressure and humidity levels. From the computational perspective, EpiGraph is a network I/O bound application written in C language and implemented on MPI. Internally, EpiGraph performs irregular memory access patterns related to sparse matrices used to model the individual interactions.</p> <p>This distribution contains the meteorological-based experimental data used to run the experiments for the paper entitled <strong><em>Evaluating the impact of the weather conditions on the influenza propagation</em></strong></p> <p><br> In order to install this data set, you first need to install EpiGraph. You can download it from:</p> <p>https://gitlab.arcos.inf.uc3m.es:8380/desingh/EpiGraph</p> <p>Then, to install this package take the following steps:</p> <p>1.- Download the file and place it in EpiGraphHome directory.</p> <p>2.- Extract the tarbal in this directory. </p> <p>     tar -zxvf EpiGraph.1.3.DataMeteo.tar.gz</p

Lindaview: an OBDA-based tool for self-sufficiency assessment

Poverty and social exclusion are a reality in every society. They are complex problems that require updated information and access to scattered data sources to make a proper assessment of a person’s situation. To help social workers with these tasks, we developed the Lindaview tool at the suggestion of the Social Services Department of the Barcelona City Council. Assessment of individuals seeking social assistance is not standardized, as it depends entirely on the social worker’s perception and experience. We design a tool that provides an informed starting point for the assessment of an individual’s self-sufficiency. Furthermore, we included a section of general statistics, allowing policymakers to access comprehensive, updated, and timely information, empowering them to make data-based decisions when allocating available resources. Lindaview is an OBDA-based tool. OBDA (Ontology- Based Data Access) is a paradigm that allows accessing data from its original source without data migration or updates on the original data architecture. Moreover, with this paradigm, we can infer implicit information via ontology reasoning

Real COVID-19 incidence rate estimate in Spain

[ES] Introducción: Los modelos epidemiológicos han demostrado ser cruciales para apoyar la toma de decisiones de las autoridades sanitarias durante la pandemia de COVID-19, así como concienciar al público en general de las distintas medidas adoptadas por las autoridades (distanciamiento social, uso de mascarilla, vacunación, etc.). Objetivos: Describir la metodología para integrar diferentes fuentes de datos para generar una única serie temporal que proporciona tasas de incidencia reales de COVID-19 en España. Metodología: Esta serie considera tanto los casos notificados como los no notificados, es decir, aquellos que no han sido registrados por las autoridades sanitarias. Resultados: Este trabajo describe también cómo la información generada en este proyecto ha sido tratada y almacenada, presenta los datos de estimación de la incidencia real obtenidos, así como los organismos y equipos de investigación que la utilizan, además de los distintos canales de comunicación que han sido empleados para difundirla (página web, compartición de resultados con las autoridades sanitarias, y repositorio). Conclusión: Este trabajo integra información proveniente de múltiples fuentes de datos para el análisis y la predicción de la incidencia de la COVID-19. A través de un enfoque multidisciplinar, se ha logrado plantear respuesta a la problemática en la estimación de la incidencia real de casos de COVID-19. [EN] Introduction: Epidemiological models have proven to be crucial in supporting the decision-making of health authorities during the COVID-19 pandemic as well as raising awareness among the general public of the different measures adopted by authorities (social distancing, mask usage, vaccination, etc.). Objectives: This work describes the methodology to integrate different data sources to generate a single time series that provides real incidence rates of COVID-19 in Spain. Methodology: This series considers both reported and non-notified cases, that is, those that have not been registered by health authorities. Results: This work also describes how the information generated in this project has been treated and stored, it presents the estimated real incidence data obtained, as well as the organizations and research teams that use it, and the different communication channels that have been used to disseminate it (webpage, sharing results with health authorities, and repository). Conclusion: This work integrates information from multiple data sources for the analysis and prediction of the incidence of COVID-19. Through a multidisciplinary approach, it has been possible to propose a response to the problem of estimating the real incidence of COVID-19 cases.Keywords: COVID-19; nowcasting; epidemiological models.Este trabajo ha sido financiado mediante el Convenio firmado entre la Comunidad de Madrid (Consejería de Educación, Universidades, Ciencia y Portavocía) y la Universidad Carlos III de Madrid para la concesión directa de una ayuda para financiar la realización de actuaciones en materia de investigación sobre el SARS-COV 2 y la enfermedad COVID-19 financiado con los recursos REACT-UE del fondo europeo de desarrollo regional y el proyecto BCV-2022-1-0005 de la Red Española de Supercomputación.S

Impact of late presentation of HIV infection on short-, mid- and long-term mortality and causes of death in a multicenter national cohort : 2004-2013

To analyze the impact of late presentation (LP) on overall mortality and causes of death and describe LP trends and risk factors (2004-2013). Cox models and logistic regression were used to analyze data from a nation-wide cohort in Spain. LP is defined as being diagnosed when CD4 < 350 cells/ml or AIDS. Of 7165 new HIV diagnoses, 46.9% (CI:45.7-48.0) were LP, 240 patients died.First-year mortality was the highest (aHR = 10.3[CI:5.5-19.3]); between 1 and 4 years post-diagnosis, aHR = 1.9(1.2-3.0); an

Prediction of long-term outcomes of HIV-infected patients developing non-AIDS events using a multistate approach

Outcomes of people living with HIV (PLWH) developing non-AIDS events (NAEs) remain poorly defined. We aimed to classify NAEs according to severity, and to describe clinical outcomes and prognostic factors after NAE occurrence using data from CoRIS, a large Spanish HIV cohort from 2004 to 2013. Prospective multicenter cohort study. Using a multistate approach we estimated 3 transition probabilities: from alive and NAE-free to alive and NAE-experienced ("NAE development"); from alive and NAE-experienced to death ("Death after NAE"); and from alive and NAE-free to death ("Death without NAE"). We analyzed the effect of different covariates, including demographic, immunologic and virologic data, on death or NAE development, based on estimates of hazard ratios (HR). We focused on the transition "Death after NAE". 8,789 PLWH were followed-up until death, cohort censoring or loss to follow-up. 792 first incident NAEs occurred in 9.01% PLWH (incidence rate 28.76; 95% confidence interval [CI], 26.80-30.84, per 1000 patient-years). 112 (14.14%) NAE-experienced PLWH and 240 (2.73%) NAE-free PLWH died. Adjusted HR for the transition "Death after NAE" was 12.1 (95%CI, 4.90-29.89). There was a graded increase in the adjusted HRs for mortality according to NAE severity category: HR (95%CI), 4.02 (2.45-6.57) for intermediate-severity; and 9.85 (5.45-17.81) for serious NAEs compared to low-severity NAEs. Male sex (HR 2.04; 95% CI, 1.11-3.84), ag