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                <text>We investigate the spreading dynamics of infected cases for SARS-2013 and COVID-19 epidemics for different regions of the world, in terms of the renormalization group language. The latter provides an alternative way to describe the underlying dynamics of disease spread. This allows us to introduce important quantities, for a given disease, such as the slope of the beta function at fixed points and the time scale of the epidemic spread inflection point. We discover that for COVID-19 the epidemic slope is of order one inverse week and the inflection point occurs roughly 4 weeks after the outbreak. We use these results to attempt long term estimates for the epidemic evolution in several regions of the world. The accuracy of the results vary depending on the epidemic stage for each region. We also provide a webpage where we daily update our analyses.</text>
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                <text>Frontiers in Physics</text>
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                <text>DOI: 10.3389/fonc.2020.00734</text>
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                <text>Jhony A. De La Cruz Vargas</text>
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                <text>The epidemic and pandemic of COVID-19 constitute an unprecedented public health emergency, it arrived in Latin America, first in Brazil, then in Ecuador; The first cases in Peru were registered as of March 6, 2020. The correct response of the Government of Peru decreed a state of national emergency and mandatory social isolation, initially for 14 days, with the aim of reducing the exposure and transmission of the Coronavirus. It assigned economic funds for 3 million poor families in Peru, during social isolation.The repercussions of COVID-19 are huge, at a social, economic, cultural, and educational level, of course, on the country's health system and your staff. Borders, universities, shops, restaurants are closed. The Pension Fund Administrators (AFP for its acronym in Spanish) announced to be affected quickly, generating significant losses to users. All of this is unpredictable and, just the tip of the iceberg.</text>
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                <text>DOI: 10.25176/RFMH.v20i2.2913</text>
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                <text>Revista de la Facultad de Medicina Humana</text>
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                <text>Universidad Ricardo Palma</text>
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                <text>Since its first confirmed case at the end of 2019, COVID-19 has become a global pandemic in three months with more than 1.4 million confirmed cases worldwide, as of early April 2020. Quantifying the changes of pollutant emissions due to COVID-19 and associated governmental control measures is crucial to understand its impacts on economy, air pollution, and society. We used the WRF-GC model and the tropospheric NO2 column observations retrieved by the TROPOMI instrument to derive the top-down NOx emission change estimation between the three periods: P1 (January 1st to January 22nd, 2020), P2 (January 23rd, Wuhan lockdown, to February 9th, 2020), and P3 (February 10th, back-to-work day, to March 12th, 2020). We found that NOx emissions in East China averaged during P2 decreased by 50% compared to those averaged during P1. The NOx emissions averaged during P3 increased by 26% compared to those during P2. Most provinces in East China gradually regained some of their NOx emissions after February 10, the official back-to-work day, but NOx emissions in most provinces have not yet to return to their previous levels in early January. NOx emissions in Wuhan, the first epicenter of COVID-19, had no sign of emission recovering by March 12. A few provinces, such as Zhejiang and Shanxi, have recovered fast, with their averaged NOx emissions during P3 almost back to pre-lockdown levels.</text>
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                <text>DOI: 10.3390/atmos11040433</text>
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                <text>DOI: 10.3389/fpubh.2020.00151</text>
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                <text>Frontiers in Public Health</text>
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                <text>Chloe Hyun-Jung Lee, Hashem Koohy</text>
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                <text>Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.</text>
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                <text>DOI: 10.12688/f1000research.22507.1</text>
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                <text>DOI: 10.1186/s13054-020-02880-z</text>
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                  <text>Dominio científico: Coronavirus</text>
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                <text>Critical Care</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="16946">
                <text>BMC</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="38">
            <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>
            <elementTextContainer>
              <elementText elementTextId="16947">
                <text>Medical emergencies. Critical care. Intensive care. First aid</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="44">
            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="16948">
                <text>EN</text>
              </elementText>
            </elementTextContainer>
          </element>
        </elementContainer>
      </elementSet>
    </elementSetContainer>
  </item>
</itemContainer>
