<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-14T02:23:02Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:10230/46565" metadataPrefix="marc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:10230/46565</identifier><datestamp>2025-12-22T20:19:23Z</datestamp><setSpec>com_2072_6</setSpec><setSpec>col_2072_452954</setSpec></header><metadata><record xmlns="http://www.loc.gov/MARC21/slim" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
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      <subfield code="a">Saiz Vila, Clàudia</subfield>
      <subfield code="e">author</subfield>
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      <subfield code="c">2021-02-22T10:22:23Z</subfield>
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      <subfield code="c">2021-02-22T10:22:23Z</subfield>
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      <subfield code="c">2020-09</subfield>
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      <subfield code="a">Treball de fi de grau en Biomèdica</subfield>
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      <subfield code="a">Tutor: Ralph G. Andrzejak</subfield>
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      <subfield code="a">At the end of December 2019, it was found that a new coronavirus (SARS-CoV-2) was&#xd;
causing pneumonia-like illness in the city of Wuhan, China. This virus started to spread&#xd;
very rapidly causing a global large-scale infection. The Covid-19 pandemic has produced&#xd;
and it is still generating a brutal impact on society, forcing the lockdown of many&#xd;
countries as well as the collapse of their healthcare system, leading to a considerable&#xd;
growth in the number of deaths. During the outbreak, most of the information and&#xd;
dynamics of the virus was unknown and unpredictable. Therefore, the proposed study&#xd;
aims to create a stochastic mathematical model based on probabilities to estimate the&#xd;
dynamics of the outbreak of the Covid-19 pandemic using the available public domain&#xd;
data. By estimating the probabilities of getting the infection and subsequently recovering&#xd;
or dying from it, the epidemic curves of the cumulative sum of detected infected cases,&#xd;
recoveries and deaths were simulated for Germany, Italy and South Korea from 22nd&#xd;
January to 30th June 2020. Furthermore, using the outputs provided by the proposed&#xd;
model, a more accurate case fatality ratio was calculated and different lockdown scenarios&#xd;
such as its anticipation or delay were discussed. Results have been analyzed with respect&#xd;
to the political and healthcare strategies that each country has followed during the&#xd;
pandemic.</subfield>
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      <subfield code="a">Covid-19</subfield>
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      <subfield code="a">SARS-CoV-2</subfield>
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      <subfield code="a">Mathematical model</subfield>
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      <subfield code="a">Probability</subfield>
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      <subfield code="a">Dynamics</subfield>
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      <subfield code="a">Epidemic curves</subfield>
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      <subfield code="a">Case fatality ratio</subfield>
   </datafield>
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      <subfield code="a">Lockdown</subfield>
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      <subfield code="a">A Mathematical Model to Estimate the Dynamics of the Covid-19 Pandemic Using&#xd;
the Ongoing Public Domain Data</subfield>
   </datafield>
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