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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A nonparametric estimation of the infection curve</title><author><name sortKey="Lin, Huazhen" sort="Lin, Huazhen" uniqKey="Lin H" first="Huazhen" last="Lin">Huazhen Lin</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.443347.3</institution-id><institution>School of Statistics,</institution><institution>Southwestern University of Finance and Economics,</institution></institution-wrap>Chengdu, 611130 China</nlm:aff></affiliation></author><author><name sortKey="Yip, Paul S F" sort="Yip, Paul S F" uniqKey="Yip P" first="Paul S. F." last="Yip">Paul S. F. Yip</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.194645.b</institution-id><institution-id institution-id-type="ISNI">0000000121742757</institution-id><institution>Social Work and Social Administration,</institution><institution>University of Hong Kong,</institution></institution-wrap>Hong Kong, China</nlm:aff></affiliation></author><author><name sortKey="Huggins, Richard M" sort="Huggins, Richard M" uniqKey="Huggins R" first="Richard M." last="Huggins">Richard M. Huggins</name><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="GRID">grid.1008.9</institution-id><institution-id institution-id-type="ISNI">000000012179088X</institution-id><institution>Department of Mathematics and Statistics,</institution><institution>The University of Melbourne,</institution></institution-wrap>Melbourne, Australia</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32214992</idno><idno type="pmc">7089265</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089265</idno><idno type="RBID">PMC:7089265</idno><idno type="doi">10.1007/s11425-011-4224-7</idno><date when="2011">2011</date><idno type="wicri:Area/Pmc/Corpus">000465</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000465</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A nonparametric estimation of the infection curve</title><author><name sortKey="Lin, Huazhen" sort="Lin, Huazhen" uniqKey="Lin H" first="Huazhen" last="Lin">Huazhen Lin</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.443347.3</institution-id><institution>School of Statistics,</institution><institution>Southwestern University of Finance and Economics,</institution></institution-wrap>Chengdu, 611130 China</nlm:aff></affiliation></author><author><name sortKey="Yip, Paul S F" sort="Yip, Paul S F" uniqKey="Yip P" first="Paul S. F." last="Yip">Paul S. F. Yip</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.194645.b</institution-id><institution-id institution-id-type="ISNI">0000000121742757</institution-id><institution>Social Work and Social Administration,</institution><institution>University of Hong Kong,</institution></institution-wrap>Hong Kong, China</nlm:aff></affiliation></author><author><name sortKey="Huggins, Richard M" sort="Huggins, Richard M" uniqKey="Huggins R" first="Richard M." last="Huggins">Richard M. Huggins</name><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="GRID">grid.1008.9</institution-id><institution-id institution-id-type="ISNI">000000012179088X</institution-id><institution>Department of Mathematics and Statistics,</institution><institution>The University of Melbourne,</institution></institution-wrap>Melbourne, Australia</nlm:aff></affiliation></author></analytic><series><title level="j">Science China. Mathematics</title><idno type="ISSN">1674-7283</idno><idno type="eISSN">1869-1862</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Predicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals. However, while back-projection techniques allow reliable estimation of the numbers of infected individuals in the more distant past, they are less reliable in the recent past. We propose two new nonparametric methods to estimate the unobserved numbers of infected individuals in the recent past in an epidemic. The proposed methods are noniterative, easily computed and asymptotically normal with simple variance formulas. Simulations show that the proposed methods are much more robust and accurate than the existing back projection method, especially for the recent past, which is our primary interest. We apply the proposed methods to the 2003 Severe Acute Respiratory Syndorme (SARS) epidemic in Hong Kong.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Anderson, R M" uniqKey="Anderson R">R. M. Anderson</name></author><author><name sortKey="Fraserl, C" uniqKey="Fraserl C">C. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Sci China Math</journal-id><journal-id journal-id-type="iso-abbrev">Sci China Math</journal-id><journal-title-group><journal-title>Science China. Mathematics</journal-title></journal-title-group><issn pub-type="ppub">1674-7283</issn><issn pub-type="epub">1869-1862</issn><publisher><publisher-name>SP Science China Press</publisher-name><publisher-loc>Heidelberg</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32214992</article-id><article-id pub-id-type="pmc">7089265</article-id><article-id pub-id-type="publisher-id">4224</article-id><article-id pub-id-type="doi">10.1007/s11425-011-4224-7</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group></article-categories><title-group><article-title>A nonparametric estimation of the infection curve</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Lin</surname><given-names>HuaZhen</given-names></name><address><email>linhz@swufe.edu.cn</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Yip</surname><given-names>Paul S. F.</given-names></name><address><email>sfpyip@hku.hk</email></address><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Huggins</surname><given-names>Richard M.</given-names></name><address><email>huggins@ms.unimelb.edu.au</email></address><xref ref-type="aff" rid="Aff3">3</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="GRID">grid.443347.3</institution-id><institution>School of Statistics,</institution><institution>Southwestern University of Finance and Economics,</institution></institution-wrap>Chengdu, 611130 China</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="GRID">grid.194645.b</institution-id><institution-id institution-id-type="ISNI">0000000121742757</institution-id><institution>Social Work and Social Administration,</institution><institution>University of Hong Kong,</institution></institution-wrap>Hong Kong, China</aff><aff id="Aff3"><label>3</label><institution-wrap><institution-id institution-id-type="GRID">grid.1008.9</institution-id><institution-id institution-id-type="ISNI">000000012179088X</institution-id><institution>Department of Mathematics and Statistics,</institution><institution>The University of Melbourne,</institution></institution-wrap>Melbourne, Australia</aff></contrib-group><pub-date pub-type="epub"><day>4</day><month>8</month><year>2011</year></pub-date><pub-date pub-type="ppub"><year>2011</year></pub-date><volume>54</volume><issue>9</issue><elocation-id>1815</elocation-id><history><date date-type="received"><day>3</day><month>10</month><year>2010</year></date><date date-type="accepted"><day>25</day><month>1</month><year>2011</year></date></history><permissions><copyright-statement>© Science China Press and Springer-Verlag Berlin Heidelberg 2011</copyright-statement><license><license-p>This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.</license-p></license></permissions><abstract id="Abs1"><p>Predicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals. However, while back-projection techniques allow reliable estimation of the numbers of infected individuals in the more distant past, they are less reliable in the recent past. We propose two new nonparametric methods to estimate the unobserved numbers of infected individuals in the recent past in an epidemic. The proposed methods are noniterative, easily computed and asymptotically normal with simple variance formulas. Simulations show that the proposed methods are much more robust and accurate than the existing back projection method, especially for the recent past, which is our primary interest. 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