Document Zbl 1495.00077

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(ed.)</a></div> <h2 class="title"> <strong>Introduction to the special issue on data science for COVID-19.</strong> <i>(English)</i> <a class="label nowrap" href="/1495.00077">Zbl 1495.00077</a> </h2> <div class="source"> <a href="/serials/2233" title="Journal Profile">J. Nonparametric Stat.</a> <a href="/?q=in%3A487273" title="Articles in this Issue">34, No. 3, 555-569 (2022)</a>. </div> <div class="abstract">Summary: An introduction to this Special Issue on Data Science for COVID-19 is included in this paper. It contains a general overview about methods and applications of nonparametric inference and other flexible data science methods for the COVID-19 pandemic. Specifically, some methods existing before the COVID-19 outbreak are surveyed, followed by an account of survival analysis methods for COVID-related times. Then, several nonparametric tools for the estimation of certain COVID rates are revised, along with the forecasting of most relevant series counts, and some other related problems. Within this setup, the papers published in this special issue are briefly commented in this introductory article.</div> <div class="clear"></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A00B15" title="MSC2020">00B15</a> </td> <td class="space"> Collections of articles of miscellaneous specific interest </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A62-06" title="MSC2020">62-06</a> </td> <td class="space"> Proceedings, conferences, collections, etc. pertaining to statistics </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A62P10" title="MSC2020">62P10</a> </td> <td class="space"> Applications of statistics to biology and medical sciences; meta analysis </td> </tr></table> </div> <div class="software"> <h3>Software:</h3><a href="/software/24733">epinet</a></div>  <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div class="form-group"> <label for="metadataText" class="control-label">Result</label> <textarea class="form-control" id="metadataText" rows="10" style="min-width: 100%;max-width: 100%"></textarea> </div> <div id="metadata-alert" class="alert alert-danger" role="alert" style="display: none;">  </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" onclick="copyMetadata()">Copy to clipboard</button> <button type="button" class="btn btn-default" data-dismiss="modal">Close</button> </div> </div> </div> </div> <div class="functions clearfix"> <div class="function">  <a type="button" class="btn btn-default btn-xs pdf" data-toggle="modal" data-target="#metadataModal" data-itemtype="Zbl" data-itemname="Zbl 1495.00077" data-ciurl="/ci/07590591" data-biburl="/bibtex/07590591.bib" data-amsurl="/amsrefs/07590591.bib" data-xmlurl="/xml/07590591.xml" > Cite </a> <a class="btn btn-default btn-xs pdf" data-container="body" type="button" href="/pdf/07590591.pdf" title="Zbl 1495.00077 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1080/10485252.2022.2108288" aria-label="DOI for “Introduction to the special issue on data science for COVID-19”" title="10.1080/10485252.2022.2108288">DOI</a> </div> <div class="sfx" style="float: right;"> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Abbasov, A. 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