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            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Coronavirus</text>
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                <text>Dominio científico: Coronavirus</text>
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          <name>Title</name>
          <description>A name given to the resource</description>
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              <text>Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications</text>
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          <name>Creator</name>
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              <text>Carlos T. Calafate, Juan-Carlos Cano, Pietro Manzoni, Enrique Hernández-Orallo</text>
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          <description>An account of the resource</description>
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              <text>One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and detect new possibly-infected individuals. This paper evaluates the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts. We study how these applications work in order to model the main aspects that can affect their performance: precision, utilization, tracing speed and implementation model (centralized vs. decentralized). Then, we propose an epidemic model to evaluate their efficiency in terms of controlling future outbreaks and the effort required (e.g., individuals quarantined). Our results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number R0 (for example, social distancing). Furthermore, we have found that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak. On the contrary, a decentralized model would require a higher utilization to be effective.</text>
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          <name>Date</name>
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              <text>2020</text>
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          <name>Subject</name>
          <description>The topic of the resource</description>
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              <text>covid-19, epidemic modeling, mobile computing, mobile crowdsensing, opportunistic networking, digital epidemiology</text>
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          <name>Identifier</name>
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              <text>10.3390/app10207113</text>
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          <name>Source</name>
          <description>A related resource from which the described resource is derived</description>
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              <text>Epidemiology and Health</text>
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          <name>Publisher</name>
          <description>An entity responsible for making the resource available</description>
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              <text>Korean Society of Epidemiology</text>
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          <name>Coverage</name>
          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <text>Biology (General), Chemistry, Engineering (General). Civil engineering (General), Technology, Physics</text>
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