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The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
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  • Open Access English
    Authors: 
    Rosati, Germán; Domenech, Laia; Chazarreta, Adriana Silvina; Maguire, Tomás;
    Country: Argentina

    We present a first approximation to the quantification of social representations about the COVID-19, using news comments. A web crawler was developed to construct the dataset of reader’s comments. We detect relevant topics in the dataset using Latent Dirichlet Allocation, and analyze its evolution during time. Finally, we show a first prototype to the prediction of the majority topics, using FastText. Sociedad Argentina de Informática

  • Open Access English
    Authors: 
    Díaz, Francisco Javier; D'Agostino, Sandra; Molinari, Lía Hebe; Osorio, Alejandra; Amadeo, Ana Paola; Vaena, Rubén Abel;
    Country: Argentina

    The Phone Line 148 (in spanish, Centro de Atencion Integral Telefonica-linea 148, CAIT) of the de Government of Province of Buenos Aires (Argentina) is the telephone communication channel with citizens for consultations about general provincial procedures. During the COVID-19 pandemic, the phone line 148 is one of the first contacts between a person that believes to be infected and the Health Care System. It is the principal place of the registry of suspected cases of infection and close contact cases. In addition, it provides general information about situations related to the pandemic. The use of a virtual learning tool as Moodle, facilitated the formation of a community to establish a uniform discourse before citizens and guarantee immediate communication in the face of new guidelines or emerging situations. Facultad de Informática

  • Open Access English
    Authors: 
    Perdomo, Luciano; Ordinez, Leonardo;
    Country: Argentina

    In Argentina there was a great growth of e-commerce due to the COVID-19 pandemic. With the aim of helping local companies to understand the market and help them in decision making, data were obtained from online shoe sales sites and with them Machine Learning models were implemented to make price predictions in sneakers. It was concluded that higher-tier companies have greater competitive advantage over lower-tier companies. Nonetheless, the cost-effective methodology used would aid local companies scale up. Workshop: WBDMD - Base de Datos y Minería de Datos Red de Universidades con Carreras en Informática

  • Open Access English
    Authors: 
    Alvarado, Yoselie; Rodríguez, Graciela; Jofré, Nicolás; Fernández, Jacqueline; Guerrero, Roberto A.;
    Country: Argentina

    Some time ago Virtual Reality and Augmented Reality were exclusively devoted to the gaming industry. Nowadays, both technologies are experiencing a deep interest from various spheres, including healthcare sector. The new infectious disease COVID-19 has had a catastrophic effect on the world’s demographics. Many patients with mild or severe COVID- 19 do not recover completely and present with a wide variety of chronic symptoms after infection, often of a neurological, cognitive or psychiatric nature. The most common signs of cognitive disorder can be summarized as mental fog, memory problems and concentration problems. The aim of this study was to analyze the opportunities for Virtual and Augmented Reality in the cognitive interventions related to mentioned disorders by searching for articles in scientific databases. We conclude that as these technologies and devices become cheaper and accessible worldwide, can at least be regarded as a rehabilitation therapy as effective as traditional training, and to some extent better than it. Workshop: WCGIV - Computación Gráfica, Imágenes y Visualización Red de Universidades con Carreras en Informática

  • Open Access English
    Authors: 
    Rodríguez, Mariela E.; Boixader, Francesc; Wong, Alvaro; Rexachs del Rosario, Dolores; Luque, Emilio;
    Country: Argentina

    Simulation, in health services, has become an important tool that has made it possible to replicate real scenarios that have a critical degree and thus have reliable information to obtain knowledge about the variables managed in an emergency. In the activity of a Hospital Emergency Service (HED), we can find active agents such as doctors, nurses, patients and passive ones such as medical rooms, beds, among others, interacting between them. HEDs have been overwhelmed by catastrophic events, such as those induced by COVID 19, in the last two years. In these situations, the availability of resources, due to the considerable reduction in health personnel and the excessive arrival of patients for care, implies that the emergency service has become inoperative. The recovery of this essential service is fundamental and a priority. For this reason, it is necessary to analyze the period of time for departments, after a catastrophe, to reach a stable state that allows them to adapt to the new workload, that is, to analyze how resilient the ED is. The present work shows the proposal of a research project for the modeling and simulation of an ED that supports the design and evaluation of a resilient service considering the infrastructures, resources and services that occur in an ED. The model must consider the organization and infrastructure capacity to support and recover from extreme situations, in order to provide and maintain its operation and services in an adequate manner. To achieve this objective, the normal service index and the ability to resist, restore and evolve in the face of future risks and threats will be measured. The design of the model will be integrated and participatory, and it will focus on people and communities, governed by the principles of sustainable development goals regarding Health and Wellbeing, Gender Equality, Industry, Innovation and Infrastructure. Instituto de Investigación en Informática

  • Open Access English
    Authors: 
    Bellassai, Juan C.; Madoery, Pablo G.; Detke, Ramiro; Blanco, Lucas; Comerci, Sandro; Marattin, María S.; Fraire, Juan; González Montoro, Aldana; Britos,Grisel; Ojeda, Silvia; +1 more
    Country: Argentina

    In the context of COVID-19, contact tracing has shown its value as a tool for contention of the pandemic. In addition to its paper based form, contact tracing can be carried out in a more scalable and faster way by using digital apps. Mobile phones can record digital signals emitted by communication and sensing technologies, enabling the identification of risky contacts between users. Factors such as proximity, encounter duration, environment, ventilation, and the use (or not) of protective measures contribute to the probability of contagion. Estimation of these factors from the data collected by phones remains a challenge. In this work in progress we describe some of the challenges of digital contact tracing, the type of data that can be collected with mobile phones and focus particularly on the problem of proximity estimation using Bluetooth Low Energy (BLE) signals. Specifically, we use machine learning models fed with different combinations of statistical features derived from the BLE signal and study how improvements in accuracy can be obtained with respect to reference models currently in use. Sociedad Argentina de Informática e Investigación Operativa

  • Open Access English
    Authors: 
    Toledo Margalef, Pablo; Balcazar, Emanuel; Ordinez, Leonardo; Delrieux, Claudio; Allende, Lucila;
    Country: Argentina

    The present work exposes preliminary results on utilizing web scraping and data mining techniques to analyze news articles published during the COVID-19 pandemic in the Chubut province. Analysis of extracted articles was made using Latent Dirichlet Allocation obtaining promising results. Facultad de Informática