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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JDS</journal-id>
      <journal-title-group>
        <journal-title>Journal of Data Science</journal-title>
      </journal-title-group>
      <issn pub-type="epub">1680-743X</issn>
      <issn pub-type="ppub">1680-743X</issn>
      <publisher>
        <publisher-name>SOSRUC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">S02</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.202012_18(5).0002</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>On Identification of High Risk Carriers of COVID-19 Using Masked Mobile Device Data</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Huang</surname>
            <given-names>Da</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">School of Management, Fudan University, Shanghai, China</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Zhu</surname>
            <given-names>Xuening</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Institute for Big Data, Fudan University, Shanghai, China</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Luo</surname>
            <given-names>Weidong</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_002"/>
        </contrib>
        <aff id="j_JDS_aff_002">Aurora Moble Ltd., Shenzhen, Guangdong, China</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Yin</surname>
            <given-names>Hao</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_003"/>
        </contrib>
        <aff id="j_JDS_aff_003">Aurora Moble Ltd., Shenzhen, Guangdong, China</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Hong</surname>
            <given-names>Jing</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_004"/>
        </contrib>
        <aff id="j_JDS_aff_004">Aurora Moble Ltd., Shenzhen, Guangdong, China</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Chen</surname>
            <given-names>Yu</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_005"/>
        </contrib>
        <aff id="j_JDS_aff_005">Guanghua School of Management, Peking University, Beijing China</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Zhou</surname>
            <given-names>Jing</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_006"/>
        </contrib>
        <aff id="j_JDS_aff_006">School of Statistics, Renmin University of China, Beijing China</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Wang</surname>
            <given-names>Hansheng</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_007"/>
        </contrib>
        <aff id="j_JDS_aff_007">Guanghua School of Management, Peking University, Beijing China</aff>
      </contrib-group>
      <volume>18</volume>
      <issue>5</issue>
      <fpage>849</fpage>
      <lpage>859</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Millions of people travel from Wuhan to other cities from Jan. 1st 2020 to Jan 23rd 2020. Taking advantage of the masked software development kit data from Aurora Mobile Ltd and open epidemic data released by health authorities, we analyze the relationship between number of confirmed COVID-19 cases in a region and the people who traveled from Wuhan to this region in this period. Further, we identify high risk carriers of COVID-19 to improve the control of COVID-19. The key findings are three-folds: (1) in each region the number of high-risk carriers is highly positively correlated with the severity of illness; (2) history of visit to the 62 designated hospitals is the foremost index of risk; (3) the second most important index is the travelers’ duration of stay in Wuhan. Based on our analysis, we estimate that, as of February 4, 2020, (a) among the 8.5 million people held up in Wuhan, there are 425 thousand high risk carriers; and (b) among all the 3.5 million migrant workers held up in Hubei, there are 175 thousand high risk carriers. The disease control authorities should closely minotor these groups.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>mobile device software development kit</kwd>
        <kwd>risk scoring</kwd>
        <kwd>SARS-CoV-2</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
