SARS Epidemiology Modeling
Identifieur interne : 000A75 ( Pmc/Corpus ); précédent : 000A74; suivant : 000A76SARS Epidemiology Modeling
Auteurs : Ying-Hen Hsieh ; Jen-Yu Lee ; Hsiao-Ling ChangSource :
- Emerging Infectious Diseases [ 1080-6040 ] ; 2004.
Url:
DOI: 10.3201/eid1006.031023
PubMed: 15224675
PubMed Central: 3323156
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">SARS Epidemiology Modeling</title><author><name sortKey="Hsieh, Ying Hen" sort="Hsieh, Ying Hen" uniqKey="Hsieh Y" first="Ying-Hen" last="Hsieh">Ying-Hen Hsieh</name><affiliation><nlm:aff id="aff-1-1">National Chung Hsing University, Taichung, Taiwan;</nlm:aff></affiliation></author><author><name sortKey="Lee, Jen Yu" sort="Lee, Jen Yu" uniqKey="Lee J" first="Jen-Yu" last="Lee">Jen-Yu Lee</name><affiliation><nlm:aff id="aff-1-1">National Chung Hsing University, Taichung, Taiwan;</nlm:aff></affiliation></author><author><name sortKey="Chang, Hsiao Ling" sort="Chang, Hsiao Ling" uniqKey="Chang H" first="Hsiao-Ling" last="Chang">Hsiao-Ling Chang</name><affiliation><nlm:aff id="aff-1-2">Center for Disease Control, Taipei, Taiwan</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">15224675</idno><idno type="pmc">3323156</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323156</idno><idno type="RBID">PMC:3323156</idno><idno type="doi">10.3201/eid1006.031023</idno><date when="2004">2004</date><idno type="wicri:Area/Pmc/Corpus">000A75</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000A75</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">SARS Epidemiology Modeling</title><author><name sortKey="Hsieh, Ying Hen" sort="Hsieh, Ying Hen" uniqKey="Hsieh Y" first="Ying-Hen" last="Hsieh">Ying-Hen Hsieh</name><affiliation><nlm:aff id="aff-1-1">National Chung Hsing University, Taichung, Taiwan;</nlm:aff></affiliation></author><author><name sortKey="Lee, Jen Yu" sort="Lee, Jen Yu" uniqKey="Lee J" first="Jen-Yu" last="Lee">Jen-Yu Lee</name><affiliation><nlm:aff id="aff-1-1">National Chung Hsing University, Taichung, Taiwan;</nlm:aff></affiliation></author><author><name sortKey="Chang, Hsiao Ling" sort="Chang, Hsiao Ling" uniqKey="Chang H" first="Hsiao-Ling" last="Chang">Hsiao-Ling Chang</name><affiliation><nlm:aff id="aff-1-2">Center for Disease Control, Taipei, Taiwan</nlm:aff></affiliation></author></analytic><series><title level="j">Emerging Infectious Diseases</title><idno type="ISSN">1080-6040</idno><idno type="eISSN">1080-6059</idno><imprint><date when="2004">2004</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><pmc article-type="letter"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Emerg Infect Dis</journal-id><journal-id journal-id-type="iso-abbrev">Emerging Infect. Dis</journal-id><journal-id journal-id-type="publisher-id">EID</journal-id><journal-title-group><journal-title>Emerging Infectious Diseases</journal-title></journal-title-group><issn pub-type="ppub">1080-6040</issn><issn pub-type="epub">1080-6059</issn><publisher><publisher-name>Centers for Disease Control and Prevention</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">15224675</article-id><article-id pub-id-type="pmc">3323156</article-id><article-id pub-id-type="publisher-id">03-1023</article-id><article-id pub-id-type="doi">10.3201/eid1006.031023</article-id><article-categories><subj-group subj-group-type="heading"><subject>Letters to the Editor</subject></subj-group><subj-group subj-group-type="article-type"><subject>Letter</subject></subj-group></article-categories><title-group><article-title>SARS Epidemiology Modeling</article-title><alt-title alt-title-type="running-head">SARS Epidemiology Modeling</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Hsieh</surname><given-names>Ying-Hen</given-names></name><xref ref-type="aff" rid="aff-1-1">*</xref></contrib><contrib contrib-type="author"><name><surname>Lee</surname><given-names>Jen-Yu</given-names></name><xref ref-type="aff" rid="aff-1-1">*</xref></contrib><contrib contrib-type="author"><name><surname>Chang</surname><given-names>Hsiao-Ling</given-names></name><xref ref-type="aff" rid="aff-1-2">†</xref></contrib><aff id="aff-1-1"><label>*</label>National Chung Hsing University, Taichung, Taiwan;</aff><aff id="aff-1-2"><label>†</label>Center for Disease Control, Taipei, Taiwan</aff></contrib-group><author-notes><corresp id="cor-1-1">Address for correspondence: Ying-Hen Hsieh, Department of Applied Mathematics, National Chung Hsing University, 250 Kuo-Kuang Rd., Taichung, Taiwan 402; fax: 886-4-22853949; email: <email xlink:href="hsieh@amath.nchu.edu.tw">hsieh@amath.nchu.edu.tw</email></corresp></author-notes><pub-date pub-type="ppub"><month>6</month><year>2004</year></pub-date><volume>10</volume><issue>6</issue><fpage>1165</fpage><lpage>1167</lpage><related-article related-article-type="commentary-article" id="ra1" ext-link-type="pubmed" xlink:href="14720403" vol="9" page="1608"><string-name><surname>Zhou</surname><given-names>G</given-names></string-name>, <string-name><surname>Yan</surname><given-names>G</given-names></string-name>. <article-title>Severe acute respiratory syndrome epidemic in Asia.</article-title><source>Emerg Infect Dis</source>. <year>2003</year>;<volume>9</volume>:<fpage>1608</fpage>–<lpage>10</lpage>.</related-article><kwd-group kwd-group-type="author"><title>Keywords: </title><kwd>SARS</kwd><kwd>epidemiology</kwd><kwd>mathematical model</kwd><kwd>Taiwan</kwd><kwd>logistic-type model</kwd></kwd-group></article-meta></front><body><p><bold>To the Editor:</bold> To assess the effectiveness of intervention measures during the recent severe acute respiratory syndrome (SARS) pandemic, Zhou and Yan (<xref ref-type="bibr" rid="R-1-1"><italic>1</italic></xref>) used Richards model, a logistic-type model, to fit the cumulative number of SARS cases reported daily in Singapore, Hong Kong, and Beijing. The key to using mathematical models for SARS epidemiology is understanding the models (<xref ref-type="bibr" rid="R-1-2"><italic>2</italic></xref>). In the Richards model (<xref ref-type="bibr" rid="R-1-1"><italic>1</italic></xref>), the function <italic>F(S)</italic> in the model was described as measuring "the effectiveness of intervention measures." The parameters in <italic>F(S)</italic>, namely the maximum cases load <italic>K</italic> and the exponent of deviation <italic>a</italic>, depict the actual progression of the epidemic as described by the reported data. In other words, the parameter estimates are used to quantify end results of the intervention measures implemented during the outbreaks. Simply put, the all-important question of "what if?" was not answered by their result. To gauge the effectiveness of intervention measures, one should consider a more complicated model with variable maximum case load and growth rate (<italic>r</italic>) that highlights the time-varying nature of an epidemic and its dependence on the intervention measures implemented during the epidemic.</p><p>Predicting the trend of an epidemic from limited data during early stages of the epidemic is often futile and sometimes misleading (<xref ref-type="bibr" rid="R-1-3"><italic>3</italic></xref>). Nevertheless, early prediction of the magnitude of an epidemic outbreak is immeasurably more important than retrospective studies. But how early is too early? Intuitively, the cumulative case curve will always be S-shaped and well-described by a logistic-type model. The essential factor is the time when the inflection of the cumulative case curve occurs, i.e., the moment when a rapid increase in case numbers is replaced by a slower increase. Since the inflection point, approximated by <italic>t<sub>m</sub></italic> (<xref ref-type="bibr" rid="R-1-1"><italic>1</italic></xref>), dictates the point in time when the rate of increase of cumulative case numbers reaches its maximum, the moment marks the key turning point when the spread of the disease starts to decline. As long as the data include this inflection point and a time interval shortly after, the curve fitting and predicting future case number will be reasonably accurate.</p><p>To illustrate this point more precisely, the cumulative SARS case data by onset date in Taiwan were obtained from the SARS databank of Taiwan Center for Disease Control. The data cover the time from February 25, 2003, the onset date of the first confirmed SARS case, to June 15, 2003, the onset date of the last confirmed case; a total of 346 SARS cases were confirmed during the 2003 outbreak in Taiwan (<xref ref-type="bibr" rid="R-1-4"><italic>4</italic></xref>). The cumulative case data are fitted to the cumulative case function <italic>S(t)</italic> in Richards model with the initial time <italic>t<sub>0</sub></italic> = 0 being February 25 and the initial case number <italic>S<sub>0</sub></italic> = <italic>S(0)</italic> = 1. Description of the model, as well as the result of the parameter estimation, is shown in <xref ref-type="table" rid="T-1-A.1">Tables A1</xref>-<xref ref-type="table" rid="T-1-A.6">A6</xref>. The estimates for the parameters are <italic>r</italic> = 0.136 (95% confidence interval [CI] 0.121 to 0.150), <italic>K</italic> = 343.4 (95% CI 339.7 to 347.1), <italic>a</italic> = 1.07 (95% CI 0.80 to1.35), and the approximate inflection point at t<sub>m</sub> = 66.62 (95% CI 63.9 to 69.3) with adjusted r<sup>2</sup> >0.998, p < 0.0001 for the goodness-of-fit of the model (<xref ref-type="fig" rid="F-1-1">Figure</xref>). The result indicates that the inflection point occurred on May 3, and the estimate for the maximum case number <italic>K</italic> = 343.3 is 0.8% off the actual total case numbers.</p><fig id="F-1-1" fig-type="figure" position="float"><label>Figure</label><caption><p>SARS cases, Taiwan, 2003, using Richards model; t = real data. A, confirmed cases; B, estimated cases using the truncated data.</p></caption><graphic xlink:href="03-1023-F"></graphic></fig><p>Moreover, the case number data are sorted by onset date. Given a mean SARS incubation of 5 days (4–6 days) (<xref ref-type="bibr" rid="R-1-5"><italic>5</italic></xref>), the inflection point for SARS in Taiwan could be traced back to 5 days before May 3, namely April 28. On April 26, the first SARS patient in Taiwan died. Starting April 28, the government implemented a series of strict intervention measures, including household quarantine of all travelers from affected areas (<xref ref-type="bibr" rid="R-1-6"><italic>6</italic></xref>). In retrospect, April 28 was indeed the turning point of the SARS outbreak in Taiwan.</p><p>To address making projections during an ongoing epidemic, we used the same dataset but used various time intervals (all starting February 25) but truncated at various dates around the inflection point of May 3. The resulting parameter estimates are given in <xref ref-type="table" rid="T-1-A.1">Tables A1</xref>-<xref ref-type="table" rid="T-1-A.6">A6</xref>. For the truncated data ending on April 28 before the inflection, an unreasonable estimate of <italic>K</italic> = 875.8 was obtained. However, if we use the data ending on May 5, May 10, May 15, and May 20, we obtain estimates of <italic>K</italic> = 204.9, 253.1, 334.2, and 342.1, respectively. These estimates improve as we move further past the inflection time of May 3 (<xref ref-type="fig" rid="F-1-1">Figure</xref>). Moreover, the last estimate, using data from February 25–May 20 only, produces a 1.1% error from the eventual cumulative case number of 346, with 95% CI of 321.5 to 362.6. This retrospective exercise demonstrates that if the cumulative case data used for predictive purpose during an outbreak contain information on the inflection point and approximately 2 weeks afterwards, the estimate for the total case number can be obtained with accuracy, well before the date of the last reported case. This procedure may be immensely useful for deciding future public health policies although correctly determining the true inflection point during a real ongoing epidemic calls for scrutiny and judicious use of the model, as with all mathematical epidemic models.</p></body><back><fn-group><fn fn-type="citation"><p><italic>Suggested citation for this article</italic>: Hsieh YH, Lee JY, Chang HL. SARS epidemiology modeling. Emerg Infect Dis [serial on the Internet] 2004 June [date cited]. <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.3201/eid1006.031023">http://dx.doi.org/10.3201/eid1006.031023</ext-link></p></fn></fn-group><app-group><app><table-wrap id="T-1-A.1" position="anchor"><label>Table A1</label><caption><title>Study 1, patient characteristics, methicillin-resistant <italic>Staphylococcus aureus</italic> (MRSA), controls not infected with <italic>S. aureus</italic> and controls with methicillin-susceptible <italic>S. aureus</italic> (MSSA) surgical site infections, bivariable analyses</title></caption><table frame="hsides" rules="groups"><col width="70" span="1"></col><col width="58" span="1"></col><col width="94" span="1"></col><col width="110" span="1"></col><col width="62" span="1"></col><col width="75" span="1"></col><thead><tr><th valign="bottom" align="left" scope="col" rowspan="1" colspan="1">Variable</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Cases, MRSA (%)
(n = 121)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Controls, uninfected patients (%)
(n = 193)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">p value, (MRSA vs. uninfected controls)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Controls, MSSA (%)
(n = 165)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">p value (MRSA vs. MSSA)</th></tr></thead><tbody><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Age, mean ± SD, y</td><td valign="top" align="center" rowspan="1" colspan="1">63.9 ± 15.4</td><td valign="top" align="center" rowspan="1" colspan="1">57.3 ± 18.3</td><td valign="top" align="center" rowspan="1" colspan="1">0.001</td><td valign="top" align="center" rowspan="1" colspan="1">55.1 ± 17.4</td><td valign="top" align="center" rowspan="1" colspan="1"><0.001</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Male sex</td><td valign="top" align="center" rowspan="1" colspan="1">55 (45.5)</td><td valign="top" align="center" rowspan="1" colspan="1">92 (42.7)</td><td valign="top" align="center" rowspan="1" colspan="1">0.73</td><td valign="top" align="center" rowspan="1" colspan="1">90 (54.6)</td><td valign="top" align="center" rowspan="1" colspan="1">0.15</td></tr><tr><td colspan="6" valign="top" align="left" scope="col" rowspan="1">Coexisting conditions</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Diabetes mellitus</td><td valign="top" align="center" rowspan="1" colspan="1">59 (48.8)</td><td valign="top" align="center" rowspan="1" colspan="1">66 (34.2)</td><td valign="top" align="center" rowspan="1" colspan="1">0.01</td><td valign="top" align="center" rowspan="1" colspan="1">57 (34.6)</td><td valign="top" align="center" rowspan="1" colspan="1">0.02</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Hematologic disorder</td><td valign="top" align="center" rowspan="1" colspan="1">1 (0.8)</td><td valign="top" align="center" rowspan="1" colspan="1">1 (0.5)</td><td valign="top" align="center" rowspan="1" colspan="1">1.00</td><td valign="top" align="center" rowspan="1" colspan="1">2 (1.2)</td><td valign="top" align="center" rowspan="1" colspan="1">1.00</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">HIV infection</td><td valign="top" align="center" rowspan="1" colspan="1">0 (0.0)</td><td valign="top" align="center" rowspan="1" colspan="1">1 (0.5)</td><td valign="top" align="center" rowspan="1" colspan="1">1.00</td><td valign="top" align="center" rowspan="1" colspan="1">0</td><td valign="top" align="center" rowspan="1" colspan="1">1.00</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Hypertension</td><td valign="top" align="center" rowspan="1" colspan="1">64 (52.9)</td><td valign="top" align="center" rowspan="1" colspan="1">75 (38.9)</td><td valign="top" align="center" rowspan="1" colspan="1">0.02</td><td valign="top" align="center" rowspan="1" colspan="1">80 (48.5)</td><td valign="top" align="center" rowspan="1" colspan="1">0.48</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Liver disease</td><td valign="top" align="center" rowspan="1" colspan="1">4 (3.3)</td><td valign="top" align="center" rowspan="1" colspan="1">1 (0.5)</td><td valign="top" align="center" rowspan="1" colspan="1">0.07</td><td valign="top" align="center" rowspan="1" colspan="1">2 (1.2)</td><td valign="top" align="center" rowspan="1" colspan="1">0.25</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Malignancy</td><td valign="top" align="center" rowspan="1" colspan="1">15 (12.4)</td><td valign="top" align="center" rowspan="1" colspan="1">14 (7.3)</td><td valign="top" align="center" rowspan="1" colspan="1">0.16</td><td valign="top" align="center" rowspan="1" colspan="1">13 (7.9)</td><td valign="top" align="center" rowspan="1" colspan="1">0.23</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Obesity</td><td valign="top" align="center" rowspan="1" colspan="1">10 (8.3)</td><td valign="top" align="center" rowspan="1" colspan="1">12 (6.2)</td><td valign="top" align="center" rowspan="1" colspan="1">0.50</td><td valign="top" align="center" rowspan="1" colspan="1">18 (10.9)</td><td valign="top" align="center" rowspan="1" colspan="1">0.55</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Peripheral vascular
disease</td><td valign="top" align="center" rowspan="1" colspan="1">12 (9.9)</td><td valign="top" align="center" rowspan="1" colspan="1">3 (1.6)</td><td valign="top" align="center" rowspan="1" colspan="1">0.002</td><td valign="top" align="center" rowspan="1" colspan="1">9 (5.5)</td><td valign="top" align="center" rowspan="1" colspan="1">0.17</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Pulmonary disease</td><td valign="top" align="center" rowspan="1" colspan="1">21 (17.4)</td><td valign="top" align="center" rowspan="1" colspan="1">23 (11.9)</td><td valign="top" align="center" rowspan="1" colspan="1">0.19</td><td valign="top" align="center" rowspan="1" colspan="1">32 (19.4)</td><td valign="top" align="center" rowspan="1" colspan="1">0.76</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Renal disease</td><td valign="top" align="center" rowspan="1" colspan="1">19 (15.7)</td><td valign="top" align="center" rowspan="1" colspan="1">9 (4.7)</td><td valign="top" align="center" rowspan="1" colspan="1">0.002</td><td valign="top" align="center" rowspan="1" colspan="1">13 (7.9)</td><td valign="top" align="center" rowspan="1" colspan="1">0.06</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Transplant</td><td valign="top" align="center" rowspan="1" colspan="1">1 (0.8)</td><td valign="top" align="center" rowspan="1" colspan="1">0</td><td valign="top" align="center" rowspan="1" colspan="1">0.39</td><td valign="top" align="center" rowspan="1" colspan="1">0</td><td valign="top" align="center" rowspan="1" colspan="1">0.42</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Tobacco use</td><td valign="top" align="center" rowspan="1" colspan="1">16 (13.2)</td><td valign="top" align="center" rowspan="1" colspan="1">20 (10.4)</td><td valign="top" align="center" rowspan="1" colspan="1">0.47</td><td valign="top" align="center" rowspan="1" colspan="1">24 (14.6)</td><td valign="top" align="center" rowspan="1" colspan="1">0.86</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Alcohol abuse</td><td valign="top" align="center" rowspan="1" colspan="1">4 (3.3)</td><td valign="top" align="center" rowspan="1" colspan="1">2 (1.0)</td><td valign="top" align="center" rowspan="1" colspan="1">0.21</td><td valign="top" align="center" rowspan="1" colspan="1">6 (3.6)</td><td valign="top" align="center" rowspan="1" colspan="1">1.00</td></tr><tr><td colspan="6" valign="top" align="left" scope="col" rowspan="1">Hospital-related risk factors</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Treatment at the
academic tertiary
care hospital</td><td valign="top" align="center" rowspan="1" colspan="1">94 (77.8)</td><td valign="top" align="center" rowspan="1" colspan="1">125 (64.8)</td><td valign="top" align="center" rowspan="1" colspan="1">0.02</td><td valign="top" align="center" rowspan="1" colspan="1">109 (66.1)</td><td valign="top" align="center" rowspan="1" colspan="1">0.04</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">LOS before surgery,
median, IQR</td><td valign="top" align="center" rowspan="1" colspan="1">1, 0–4</td><td valign="top" align="center" rowspan="1" colspan="1">0, 0–3</td><td valign="top" align="center" rowspan="1" colspan="1">0.02</td><td valign="top" align="center" rowspan="1" colspan="1">0, 0–2</td><td valign="top" align="center" rowspan="1" colspan="1">0.01</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">LOS before culture,
median, IQR</td><td valign="top" align="center" rowspan="1" colspan="1">8, 5–14</td><td valign="top" align="center" rowspan="1" colspan="1">NA</td><td valign="top" align="center" rowspan="1" colspan="1">NA</td><td valign="top" align="center" rowspan="1" colspan="1">5, 3–10</td><td valign="top" align="center" rowspan="1" colspan="1"><0.001</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Proportion of patients
with an ICU stay
before surgery</td><td valign="top" align="center" rowspan="1" colspan="1">11 (9.1)</td><td valign="top" align="center" rowspan="1" colspan="1">13 (7.9)</td><td valign="top" align="center" rowspan="1" colspan="1">0.83</td><td valign="top" align="center" rowspan="1" colspan="1">18 (9.3)</td><td valign="top" align="center" rowspan="1" colspan="1">1.0</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">ASA score, median,
IQR</td><td valign="top" align="center" rowspan="1" colspan="1">3, 3–4</td><td valign="top" align="center" rowspan="1" colspan="1">3, 2–4</td><td valign="top" align="center" rowspan="1" colspan="1">0.03</td><td valign="top" align="center" rowspan="1" colspan="1">3, 2–4</td><td valign="top" align="center" rowspan="1" colspan="1">0.15</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Duration of surgery
(min), median, IQR</td><td valign="top" align="center" rowspan="1" colspan="1">240, 166–305</td><td valign="top" align="center" rowspan="1" colspan="1">194, 113–276</td><td valign="top" align="center" rowspan="1" colspan="1">0.004</td><td valign="top" align="center" rowspan="1" colspan="1">202, 116–285</td><td valign="top" align="center" rowspan="1" colspan="1">0.01</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Wound class,
median, IQR</td><td valign="top" align="center" rowspan="1" colspan="1">1, 1–1</td><td valign="top" align="center" rowspan="1" colspan="1">1, 1–1</td><td valign="top" align="center" rowspan="1" colspan="1">0.82</td><td valign="top" align="center" rowspan="1" colspan="1">1, 1–1</td><td valign="top" align="center" rowspan="1" colspan="1">0.36</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">NNIS Risk Index,
median, IQR</td><td valign="top" align="center" rowspan="1" colspan="1">1, 1–2</td><td valign="top" align="center" rowspan="1" colspan="1">1, 1–1</td><td valign="top" align="center" rowspan="1" colspan="1">0.002</td><td valign="top" align="center" rowspan="1" colspan="1">1, 1–2</td><td valign="top" align="center" rowspan="1" colspan="1">0.06</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>LOS, length of stay; IQR, interquartile range; ASA, American Society of Anesthesiologists-Physical Status score; NNIS, National Nosocomial Infections Surveillance System.</p></table-wrap-foot></table-wrap><table-wrap id="T-1-A.2" position="anchor"><label>Table A2</label><caption><title>Study 1: Adjusted outcomes models for methicillin-resistant <italic>Staphylococcus aureus</italic> (MRSA) surgical site infection (SSI) compared to uninfected control patients<sup>a</sup></title></caption><table frame="hsides" rules="groups"><col width="207" span="1"></col><col width="93" span="1"></col><col width="81" span="1"></col><col width="86" span="1"></col><thead><tr><th rowspan="2" valign="bottom" align="left" scope="col" colspan="1">Variable</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Deaths<hr></hr></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Length of stay<sup>b</sup><hr></hr></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Cost<sup>c</sup><hr></hr></th></tr><tr><th valign="bottom" colspan="1" align="center" scope="colgroup" rowspan="1">OR (95% CI)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">OR<sup>d</sup> (95% CI)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">OR (95% CI)</th></tr></thead><tbody><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">MRSA</td><td valign="top" align="center" rowspan="1" colspan="1">11.4 (2.8 to 34.9)</td><td valign="top" align="center" rowspan="1" colspan="1">3.2 (2.7 to 3.7)</td><td valign="top" align="center" rowspan="1" colspan="1">2.2 (2.0 to 2.6)</td></tr><tr><td rowspan="4" valign="top" align="left" scope="row" colspan="1">ASA score<sup>e,f</sup></td><td rowspan="4" valign="middle" align="left" colspan="1"></td><td rowspan="4" valign="top" align="center" colspan="1">1.3 (1.2 to 1.5)</td><td valign="top" align="center" rowspan="1" colspan="1">ASA score = 4
3.7 (1.5 to 8.9)</td></tr><tr><td valign="top" colspan="1" align="center" scope="col" rowspan="1">ASA score = 2
2.0 (1.4 to 2.9)</td></tr><tr><td valign="top" colspan="1" align="center" scope="col" rowspan="1">ASA score = 3
3.0 (2.1 to 4.3)</td></tr><tr><td valign="top" colspan="1" align="center" scope="col" rowspan="1">ASA Score = 4
4.1 (2.8 to 6.0)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">>73 y of age</td><td valign="top" align="center" rowspan="1" colspan="1">4.8 (2.0 to 11.6)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td colspan="4" valign="top" align="left" scope="col" rowspan="1">Operative duration (min)<sup>g</sup></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">211–400</td><td rowspan="3" valign="middle" align="left" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">(0.9 to 1.3)</td><td valign="top" align="center" rowspan="1" colspan="1">1.4 (1.2 to 1.7)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">401–590</td><td valign="top" colspan="1" align="center" rowspan="1">1.7 (1.2 to 2.4)</td><td valign="top" align="center" rowspan="1" colspan="1">2.2 (1.6 to 3.1)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">>590</td><td valign="top" colspan="1" align="center" rowspan="1">1.8 (1.1 to 2.9)</td><td valign="top" align="center" rowspan="1" colspan="1">2.6 (1.6 to 4.0)</td></tr><tr><td colspan="4" valign="top" align="left" scope="col" rowspan="1">Length of stay before surgery<sup>h</sup></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">7–13 d</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">1.6 (1.1 to 2.1)</td><td valign="top" align="center" rowspan="1" colspan="1">1.7 (1.3 to 2.3)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">14–20 d</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">3.6 (1.4 to 9.6)</td><td valign="top" align="center" rowspan="1" colspan="1">5.6 (2.3 to 13.4)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">>20 d</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">0.7 (0.2 to 2.6)</td><td valign="top" align="center" rowspan="1" colspan="1">1.2 (0.3 to 4.3)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Intensive care unit stay before surgery</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">1.5 (1.2 to 2.0)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Tertiary care hospital</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">1.5 (1.2 to 1.7)</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>OR, odds ratio; CI, confidence interval; ASA, American Society of Anesthesiologists -Physical Status.
<sup>b</sup>Model includes the following confounding variables: admission to the tertiary care hospital, diabetes, and renal disease.
<sup>c</sup>Model includes the following confounding variable: renal disease.
<sup>d</sup>For length of hospital stay and cost, OR represents multiplicative effect
<sup>e</sup>Length of stay increases by 1.3-fold for each point increase in ASA score.
<sup>f</sup>For cost, reference category is ASA score = 1.
<sup>g</sup>Reference category is operative duration < 211 min.
<sup>h</sup>Reference category is length of stay before surgery < 7 d.</p></table-wrap-foot></table-wrap><table-wrap id="T-1-A.3" position="anchor"><label>Table A3</label><caption><title>Study 1, adjusted outcomes models for methicillin-resistant <italic>Staphylococcus. aureus</italic> (MRSA) surgical site infections (SSI) compared to patients with methicillin-resistant <italic>S. aureus</italic> (MSSA) SSI<sup>a</sup></title></caption><table frame="hsides" rules="groups"><col width="250" span="1"></col><col width="72" span="1"></col><col width="72" span="1"></col><col width="72" span="1"></col><thead><tr><th valign="middle" align="left" scope="col" rowspan="1" colspan="1"></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Deaths<sup>b</sup></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Length of Stay<sup>c</sup></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Cost<sup>d</sup></th></tr></thead><tbody><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Variable</td><td valign="top" align="center" rowspan="1" colspan="1">OR (95% CI)</td><td valign="top" align="center" rowspan="1" colspan="1">OR (95% CI)<sup>e</sup></td><td valign="top" align="center" rowspan="1" colspan="1">OR<sup>e</sup> (95% CI)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">MRSA</td><td valign="top" align="center" rowspan="1" colspan="1">3.4 (1.5 to 7.7)</td><td valign="top" align="center" rowspan="1" colspan="1">1.2 (1.0 to 1.5)</td><td valign="top" align="center" rowspan="1" colspan="1"> 1.2 (1.0 to 1.4)</td></tr><tr><td rowspan="3" valign="top" align="left" scope="row" colspan="1">ASA score<sup>f</sup></td><td rowspan="3" valign="top" align="center" colspan="1">ASA score = 4
5.1 (2.1 to12.5)</td><td valign="top" align="center" rowspan="1" colspan="1">ASA score = 2
0.9 (0.5 to 1.7)</td><td valign="top" align="center" rowspan="1" colspan="1">ASA score = 2
1.0 (0.7 to 1.5)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">ASA score = 3
1.6 (0.9 to 2.9)</td><td valign="top" align="center" rowspan="1" colspan="1">ASA score = 3
1.4 (1.0 to 2.1)</td></tr><tr><td valign="top" colspan="1" align="center" scope="row" rowspan="1">Asa score = 4
1.8 (1.0 to 3.5)</td><td valign="top" align="center" rowspan="1" colspan="1">ASA score = 4
2.1 (1.4 to 3.2)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Age > 61 years</td><td valign="top" align="center" rowspan="1" colspan="1">3.0 (1.2 to 7.3)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="col" rowspan="1" colspan="1">Operative duration, min<sup>g</sup></td><td rowspan="4" valign="middle" align="left" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">206–381</td><td valign="top" colspan="1" align="center" rowspan="1">1.3 (1.0 to 1.6)</td><td valign="top" align="center" rowspan="1" colspan="1">1.4 (1.1 to 1.6)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">382–557</td><td valign="top" colspan="1" align="center" rowspan="1">1.3 (0.8 to 2.1)</td><td valign="top" align="center" rowspan="1" colspan="1">1.8 (1.3 to 2.5)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">>557</td><td valign="top" colspan="1" align="center" rowspan="1">1.1 (0.5 to 2.6)</td><td valign="top" align="center" rowspan="1" colspan="1">1.6 (0.9 to 2.8)</td></tr><tr><td valign="top" align="left" scope="col" rowspan="1" colspan="1">Length (d) of stay before infection<sup>h</sup></td><td rowspan="4" valign="middle" align="left" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">11–20</td><td valign="top" colspan="1" align="center" rowspan="1">1.4 (1.0 to 1.8)</td><td valign="top" align="center" rowspan="1" colspan="1">1.6 (1.3 to 2.0)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">21–30</td><td valign="top" colspan="1" align="center" rowspan="1">1.6 (1.0 to 2.7)</td><td valign="top" align="center" rowspan="1" colspan="1">1.7 (1.2 to 2.5)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">>30</td><td valign="top" colspan="1" align="center" rowspan="1">1.3 (0.5 to 3.1)</td><td valign="top" align="center" rowspan="1" colspan="1">1.8 (0.9 to 3.8)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Renal disease</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">1.5 (1.0 to 2.2)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="col" rowspan="1" colspan="1">Length (d) of intensive care unit stay before infection<sup>i</sup></td><td rowspan="4" valign="middle" align="left" colspan="1"></td><td rowspan="4" valign="middle" align="left" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">8–14</td><td valign="top" colspan="1" align="center" rowspan="1">1.8 (1.1, 2.8)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">15–21</td><td valign="top" colspan="1" align="center" rowspan="1">2.1 (1.1, 8.8)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">>21</td><td valign="top" colspan="1" align="center" rowspan="1">1.9 (0.4, 8.0)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Tertiary care hospital</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">1.3 (1.1, 1.6)</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>OR, odds ratio; CI, confidence interval; ASA, American Society of Anesthesiologists -Physical Status.
<sup>b</sup>Model includes the following confounding variable: operative duration >222 min.
<sup>c</sup>Model includes the following confounding variables: admission to tertiary care hospital and diabetes.
<sup>d</sup>Model includes the following confounding variables: diabetes and renal disease.
<sup>e</sup>For length of hospital stay and cost, OR represents multiplicative effect.
<sup>f</sup>For deaths, reference category is ASA score < 1; for length of stay and cost, reference category is ASA score = 1.
<sup>g</sup>Reference category is operative duration < 206 min.
<sup>h</sup>Reference category is length of stay prior to infection < 11 d.
<sup>i</sup>Reference category is intensive care unit length of stay prior to infection < 8 d.</p></table-wrap-foot></table-wrap><table-wrap id="T-1-A.4" position="anchor"><label>Table A4</label><caption><title>Study 2, patient characteristics, vancomycin-resistant enterococci (VRE) wound infections, controls not infected with enterococci, and controls with vancomycin-susceptible enterococci (VSE) wound infections, bivariate analyses</title></caption><table frame="hsides" rules="groups"><col width="105" span="1"></col><col width="70" span="1"></col><col width="76" span="1"></col><col width="88" span="1"></col><col width="83" span="1"></col><col width="48" span="1"></col><thead><tr><th valign="bottom" align="left" scope="col" rowspan="1" colspan="1">Variable</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Cases, VRE wound (%)
(n = 99)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Controls, not infected (%)
(n = 280)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">P Value
(VRE vs. controls not infected)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Controls, VSE (%) (n = 213)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">p value
(VRE vs. VSE)</th></tr></thead><tbody><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Age, mean (y)</td><td valign="top" align="center" rowspan="1" colspan="1">60.3</td><td valign="top" align="center" rowspan="1" colspan="1">63.6</td><td valign="top" align="center" rowspan="1" colspan="1">0.09</td><td valign="top" align="center" rowspan="1" colspan="1">59.1</td><td valign="top" align="center" rowspan="1" colspan="1">0.51</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Sex (female)</td><td valign="top" align="center" rowspan="1" colspan="1">46 (46)</td><td valign="top" align="center" rowspan="1" colspan="1">124 (44.3)</td><td valign="top" align="center" rowspan="1" colspan="1">0.7</td><td valign="top" align="center" rowspan="1" colspan="1">127 (59.6)</td><td valign="top" align="center" rowspan="1" colspan="1">0.03</td></tr><tr><td valign="top" align="left" scope="col" rowspan="1" colspan="1">Main diagnosis</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Orthopedic condition</td><td valign="top" align="center" rowspan="1" colspan="1">11 (11)</td><td valign="top" align="center" rowspan="1" colspan="1">30 (10.7)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">18 (8.4)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Cardiovascular condition</td><td valign="top" align="center" rowspan="1" colspan="1">25 (25)</td><td valign="top" align="center" rowspan="1" colspan="1">117 (41)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">61 (28.6)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Endocrine disorder</td><td valign="top" align="center" rowspan="1" colspan="1">3 (3)</td><td valign="top" align="center" rowspan="1" colspan="1">6 (2.1)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">4 (1.9)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Gastrointestinal disorder</td><td valign="top" align="center" rowspan="1" colspan="1">25 (25)</td><td valign="top" align="center" rowspan="1" colspan="1">60 (21.4)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">62 (29.1)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Genitourinary disorder</td><td valign="top" align="center" rowspan="1" colspan="1">6 (6)</td><td valign="top" align="center" rowspan="1" colspan="1">12 (4.2)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">9 (4.3)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Infectious disease</td><td valign="top" align="center" rowspan="1" colspan="1">16 (16)</td><td valign="top" align="center" rowspan="1" colspan="1">6 (2.1)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">20 (9.4)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Hematologic disease</td><td valign="top" align="center" rowspan="1" colspan="1">0 (0)</td><td valign="top" align="center" rowspan="1" colspan="1">2 (.7)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">0</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Neurologic disease</td><td valign="top" align="center" rowspan="1" colspan="1">11 (11)</td><td valign="top" align="center" rowspan="1" colspan="1">32 (11.4)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">34 (16)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Pulmonary disease</td><td valign="top" align="center" rowspan="1" colspan="1">2 (2)</td><td valign="top" align="center" rowspan="1" colspan="1">14 (5)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">5 (2.4)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="col" rowspan="1" colspan="1">Coexisting conditions</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Cardiovascular disease</td><td valign="top" align="center" rowspan="1" colspan="1">73 (74)</td><td valign="top" align="center" rowspan="1" colspan="1">204 (72.9)</td><td valign="top" align="center" rowspan="1" colspan="1">0.86</td><td valign="top" align="center" rowspan="1" colspan="1">150 (70.4)</td><td valign="top" align="center" rowspan="1" colspan="1">0.55</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Lung disease</td><td valign="top" align="center" rowspan="1" colspan="1">11 (11)</td><td valign="top" align="center" rowspan="1" colspan="1">33 (11.7)</td><td valign="top" align="center" rowspan="1" colspan="1">0.9</td><td valign="top" align="center" rowspan="1" colspan="1">26 (12.2)</td><td valign="top" align="center" rowspan="1" colspan="1">0.78</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Diabetes mellitus</td><td valign="top" align="center" rowspan="1" colspan="1">67 (67.7)</td><td valign="top" align="center" rowspan="1" colspan="1">139 (49.6)</td><td valign="top" align="center" rowspan="1" colspan="1">0.002</td><td valign="top" align="center" rowspan="1" colspan="1">127 (59.6)</td><td valign="top" align="center" rowspan="1" colspan="1">0.17</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Organ transplant recipient</td><td valign="top" align="center" rowspan="1" colspan="1">14 (14)</td><td valign="top" align="center" rowspan="1" colspan="1">21 (7.5)</td><td valign="top" align="center" rowspan="1" colspan="1">0.08</td><td valign="top" align="center" rowspan="1" colspan="1">18 (8.4)</td><td valign="top" align="center" rowspan="1" colspan="1">0.12</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Renal disease</td><td valign="top" align="center" rowspan="1" colspan="1">18 (18.2)</td><td valign="top" align="center" rowspan="1" colspan="1">39 (14)</td><td valign="top" align="center" rowspan="1" colspan="1">0.7</td><td valign="top" align="center" rowspan="1" colspan="1">28 (13.2)</td><td valign="top" align="center" rowspan="1" colspan="1">0.24</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Malignancy</td><td valign="top" align="center" rowspan="1" colspan="1">7 (7.1)</td><td valign="top" align="center" rowspan="1" colspan="1">27 (9.6)</td><td valign="top" align="center" rowspan="1" colspan="1">0.5</td><td valign="top" align="center" rowspan="1" colspan="1">32 (15)</td><td valign="top" align="center" rowspan="1" colspan="1">0.05</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">AIDS</td><td valign="top" align="center" rowspan="1" colspan="1">2 (2)</td><td valign="top" align="center" rowspan="1" colspan="1">2 (0.7)</td><td valign="top" align="center" rowspan="1" colspan="1">0.27</td><td valign="top" align="center" rowspan="1" colspan="1">0</td><td valign="top" align="center" rowspan="1" colspan="1">0.1</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Hepatobiliary disease</td><td valign="top" align="center" rowspan="1" colspan="1">16 (16.6)</td><td valign="top" align="center" rowspan="1" colspan="1">40 (14.3)</td><td valign="top" align="center" rowspan="1" colspan="1">0.8</td><td valign="top" align="center" rowspan="1" colspan="1">31 (14.5)</td><td valign="top" align="center" rowspan="1" colspan="1">0.71</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Charlson comorbidity score, mean</td><td valign="top" align="center" rowspan="1" colspan="1">3.17</td><td valign="top" align="center" rowspan="1" colspan="1">2.66</td><td valign="top" align="center" rowspan="1" colspan="1">0.07</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="col" rowspan="1" colspan="1">Hospital-related risk factors</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Transfer from another
institution</td><td valign="top" align="center" rowspan="1" colspan="1">34 (34.3)</td><td valign="top" align="center" rowspan="1" colspan="1">102 (36.4)</td><td valign="top" align="center" rowspan="1" colspan="1">0.5</td><td valign="top" align="center" rowspan="1" colspan="1">34 (16)</td><td valign="top" align="center" rowspan="1" colspan="1"><0.001</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Surgery</td><td valign="top" align="center" rowspan="1" colspan="1">29 (29.3)</td><td valign="top" align="center" rowspan="1" colspan="1">94 (33.6)</td><td valign="top" align="center" rowspan="1" colspan="1">0.08</td><td valign="top" align="center" rowspan="1" colspan="1">90 (42.3)</td><td valign="top" align="center" rowspan="1" colspan="1">0.03</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Admission to ICU</td><td valign="top" align="center" rowspan="1" colspan="1">26 (26.2)</td><td valign="top" align="center" rowspan="1" colspan="1">58 (20.7)</td><td valign="top" align="center" rowspan="1" colspan="1">0.9</td><td valign="top" align="center" rowspan="1" colspan="1">53 (33.3)</td><td valign="top" align="center" rowspan="1" colspan="1">0.8</td></tr></tbody></table></table-wrap><table-wrap id="T-1-A.5" position="anchor"><label>Table A5</label><caption><title>Study 2, adjusted outcomes models for vancomycin-resistant enterococcus (VRE) wound infection compared to uninfected control patients<sup>a</sup></title></caption><table frame="hsides" rules="groups"><col width="64" span="1"></col><col width="68" span="1"></col><col width="137" span="1"></col><col width="70" span="1"></col><col width="64" span="1"></col><col width="62" span="1"></col><thead><tr><th rowspan="2" valign="bottom" align="left" scope="col" colspan="1">Variable</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Deaths<sup>b</sup><hr></hr></th><th rowspan="2" valign="bottom" align="center" scope="col" colspan="1">Variable</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Length of Stay<sup>c</sup><hr></hr></th><th rowspan="2" valign="bottom" align="center" scope="col" colspan="1">Variable</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Cost<sup>d</sup><hr></hr></th></tr><tr><th valign="bottom" colspan="1" align="center" scope="colgroup" rowspan="1">OR (95% CI)</th><th valign="bottom" colspan="1" align="center" scope="colgroup" rowspan="1">OR<sup>e</sup> (95% CI)</th><th valign="bottom" colspan="1" align="center" scope="colgroup" rowspan="1">OR<sup>e</sup> (95% CI)</th></tr></thead><tbody><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">VRE infection</td><td valign="top" align="center" rowspan="1" colspan="1">2.0 (0.8 to 5.2)</td><td valign="top" align="center" rowspan="1" colspan="1">VRE infection</td><td valign="top" align="center" rowspan="1" colspan="1">1.8 (1.3 to 2.4)</td><td valign="top" align="center" rowspan="1" colspan="1">VRE infection</td><td valign="top" align="center" rowspan="1" colspan="1">1.5 (1.3, 1.8)</td></tr><tr><td valign="middle" align="left" scope="row" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">Transfer from another hospital</td><td valign="top" align="center" rowspan="1" colspan="1">1.5 (1.2 to 1.9)</td><td valign="top" align="center" rowspan="1" colspan="1">Surgery<sup>e</sup></td><td valign="top" align="center" rowspan="1" colspan="1">1.4 (1.1, 1.8)</td></tr><tr><td valign="middle" align="left" scope="row" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">Renal disease</td><td valign="top" align="center" rowspan="1" colspan="1">2.0 (1.5 to 2.7)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="middle" align="left" scope="row" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">Malignancy</td><td valign="top" align="center" rowspan="1" colspan="1">0.7 (0.5 to 0.9)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr><tr><td valign="middle" align="left" scope="row" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="top" align="center" rowspan="1" colspan="1">Intensive care unit stay<sup>f</sup></td><td valign="top" align="center" rowspan="1" colspan="1">2.3 (1.6 to 3.3)</td><td valign="middle" align="left" rowspan="1" colspan="1"></td><td valign="middle" align="left" rowspan="1" colspan="1"></td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>OR, odds ratio; CI, confidence interval.
<sup>b</sup>Model includes the following confounding variables: intensive care unit (ICU) stay and number of coexisting conditions.
<sup>c</sup>Model includes the following confounding variable: propensity score (i.e., likelihood of being a VRE case).
<sup>d</sup>Model includes the following confounding variables: propensity score [i.e., likelihood of being a VRE case (Appendix)] and length of stay before infection (index date for controls).
<sup>e</sup>For length of hospital stay and cost, OR represents multiplicative effect.
<sup>f</sup>Before infection for cases and before index date for controls.</p></table-wrap-foot></table-wrap><table-wrap id="T-1-A.6" position="anchor"><label>Table A6</label><caption><title>Study 2, adjusted outcomes models for vancomycin-resistant enterococcus (VRE) wound infection compared to control patients with wound infection due to vancomycin-susceptible enterococcus (VSE)<sup>a</sup></title></caption><table frame="hsides" rules="groups"><col width="130" span="1"></col><col width="133" span="1"></col><col width="39" span="1"></col><col width="67" span="1"></col><col width="36" span="1"></col><col width="60" span="1"></col><thead><tr><th rowspan="2" valign="bottom" align="left" scope="col" colspan="1">Variable</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Deaths<sup>b</sup><hr></hr></th><th valign="bottom" align="left" scope="col" rowspan="1" colspan="1"></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Length of Stay<sup>c</sup><hr></hr></th><th valign="bottom" align="left" scope="col" rowspan="1" colspan="1"></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Cost<sup>d</sup><hr></hr></th></tr><tr><th valign="bottom" colspan="1" align="center" scope="colgroup" rowspan="1">Odds Ratio (OR)
(95% Confidence Interval [CI])</th><th valign="bottom" align="left" scope="col" rowspan="1" colspan="1">Variable</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">OR<sup>e</sup> (95% CI)</th><th valign="bottom" align="left" scope="col" rowspan="1" colspan="1">Variable</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">OR<sup>e</sup> (95% CI)</th></tr></thead><tbody><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">VRE</td><td valign="top" align="center" rowspan="1" colspan="1">2.5 (1.1, 6.1)</td><td valign="top" align="center" rowspan="1" colspan="1">VRE</td><td valign="top" align="center" rowspan="1" colspan="1">1.1 (0.9, 1.4)</td><td valign="top" align="center" rowspan="1" colspan="1">VRE</td><td valign="top" align="center" rowspan="1" colspan="1">1.4 (1.2, 1.6)</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Intensive care unit stay (ICU)<sup>f</sup></td><td valign="top" align="center" rowspan="1" colspan="1">9.0 (3.0, 27.4)</td><td valign="top" align="center" rowspan="1" colspan="1">ICU stay<sup>f</sup></td><td valign="top" align="center" rowspan="1" colspan="1">1.8 (1.3, 2.5)</td><td valign="top" align="center" rowspan="1" colspan="1">Surgery<sup>f</sup></td><td valign="top" align="center" rowspan="1" colspan="1">1.2 (1.1, 1.3)</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>OR, odds ratio; CI, confidence interval; ICU, intensive care unit.
<sup>b</sup>Model includes the following confounding variables: gender and surgery before infection.
<sup>c</sup>Model includes the following confounding variable: malignancy and length of stay before infection.
<sup>d</sup>Model includes the following confounding variables: length of stay before cohort inclusion.
<sup>e</sup>For length of hospital stay and cost, OR represents multiplicative effect.
<sup>f</sup>Before infection for cases and before index date for controls.</p></table-wrap-foot></table-wrap></app></app-group><ref-list><title>References</title><ref id="R-1-1"><label>1. </label><mixed-citation publication-type="journal"><string-name><surname>Zhou</surname><given-names>G</given-names></string-name>, <string-name><surname>Yan</surname><given-names>G</given-names></string-name><article-title>Severe acute respiratory syndrome epidemic in Asia.</article-title><source>Emerg Infect Dis</source>. <year>2003</year>;<volume>9</volume>:<fpage>1608</fpage>–<lpage>10</lpage><pub-id pub-id-type="pmid">14720403</pub-id></mixed-citation></ref><ref id="R-1-2"><label>2. </label><mixed-citation publication-type="webpage"><string-name><surname>Hsieh</surname><given-names>YH</given-names></string-name>, <string-name><surname>Chen</surname><given-names>CWS</given-names></string-name> Re: Mathematical modeling of SARS: cautious in all our movements. 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Dis</journal-id><journal-id journal-id-type="publisher-id">EID</journal-id><journal-title-group><journal-title>Emerging Infectious Diseases</journal-title></journal-title-group><issn pub-type="ppub">1080-6040</issn><issn pub-type="epub">1080-6059</issn><publisher><publisher-name>Centers for Disease Control and Prevention</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi"> 10.3201/eid1006.040173</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>SARS Epidemiology Modeling</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Zhou</surname><given-names>Guofa</given-names></name><xref ref-type="aff" rid="aff-2-1">*</xref></contrib><contrib contrib-type="author"><name><surname>Yan</surname><given-names>Guiyan</given-names></name><xref ref-type="aff" rid="aff-2-1">*</xref></contrib><aff id="aff-2-1"><label>*</label>State University of New York, Buffalo, New York, USA</aff></contrib-group><author-notes><corresp id="cor-2-1">Address for correspondence: Guofa Zhou, Department of Biological Sciences, State University of New York, Buffalo NY 14260, USA; fax: 716-645-2975; email: <email xlink:href="gzhou2@buffalo.edu">gzhou2@buffalo.edu</email></corresp></author-notes><related-article related-article-type="commentary-article" id="ra2" ext-link-type="pubmed" xlink:href="14720403" vol="9" page="1608"><string-name><surname>Zhou</surname><given-names>G</given-names></string-name>, <string-name><surname>Yan</surname><given-names>G</given-names></string-name>. <article-title>Severe acute respiratory syndrome epidemic in Asia.</article-title><source>Emerg Infect Dis</source>. <year>2003</year>;<volume>9</volume>:<fpage>1608</fpage>–<lpage>10</lpage>.</related-article></article-meta></front><body><p><bold>In Reply:</bold> Our analysis of the dynamics of reported severe acute respiratory syndrome (SARS) clinical cases was conducted in May 2003 during the height of the public panic (<xref ref-type="bibr" rid="R-2-1"><italic>1</italic></xref>). Our primary goal in that study was to predict "when the epidemic might be brought under control if the current intervention measures were continued" (<xref ref-type="bibr" rid="R-2-1"><italic>1</italic></xref>). We used the Richards model and successfully predicted the epidemic cessation dates in Beijing, Hong Kong, and Singapore. Our predicted total number of SARS cases was close to the actual number of cases. In addition, we estimated the basic reproductive rate (R<sub>0</sub>) of SARS infection, and our estimates based on the deterministic model were similar to those based on stochastic models (<xref ref-type="bibr" rid="R-2-2"><italic>2</italic></xref><italic>,</italic><xref ref-type="bibr" rid="R-2-3"><italic>3</italic></xref>). Therefore, our analysis provided useful information on the epidemiologic characteristic of SARS infections in three major Asian cities.</p><p>Hsieh et al. (<xref ref-type="bibr" rid="R-2-4"><italic>4</italic></xref>) commented that our article did not address the effect that specific intervention measures might have on the dynamics of SARS infection. Our study was not intended to measure this. As we stated in our article, "the transmission mechanism of the coronavirus that causes SARS and the epidemiologic determinants of spread of the virus are poorly understood." Any models built on these unknowns are not suitable for assessing the effects of specific intervention measures. A method suggested by Hsieh et al. (<xref ref-type="bibr" rid="R-2-4"><italic>4</italic></xref>) to merely "consider a more complicated model with variable maximum case load and growth rate" will not answer the question to any extent.</p><p>The retrospective analysis of SARS case dynamics in Taiwan by Hsieh et al. (<xref ref-type="bibr" rid="R-2-4"><italic>4</italic></xref>) found that "as long as the data include this inflection point and time interval shortly after, the curve fitting and predicting future case number will be reasonably accurate." This notion holds only if the true inflection point is known before an epidemic ends. The main difficulty is how the true inflection point is correctly determined, as noted by Hsieh et al. (<xref ref-type="bibr" rid="R-2-4"><italic>4</italic></xref>). The time when inflection occurs varies tremendously if truncated data of cumulative SARS case numbers are used. To illustrate this point, we used the cumulative number of reported probable SARS cases in Hong Kong, starting March 17, 2003, but truncated at various dates, and calculated the date when inflection occurred (<xref ref-type="table" rid="T-2-1">Table</xref>). For example, if the data period from the onset date (March 17, 2003) to the last case reported (June 12, 2003) was used, the date when inflection would occur was estimated as March 19, 2003. If the truncated data ending April 9, April 16, April 30, May 14, and May 28, 2003, were used, the dates when inflection would occur were estimated as April 2, February 7, March 3, March 23, and April 2, 2003, respectively (<xref ref-type="table" rid="T-2-1">Table</xref>). Clearly, inflection point dates became a moving target as the epidemic progressed. When truncated data ending April 9, April 16, April 30, May 14, and May 28, 2003, were used, the corresponding estimated maximum numbers of cumulative cases (<italic>K</italic>) were 1,107, 1,907, 1,819, 1,749, and 1,733, respectively. Estimation of <italic>K</italic> improved when the data period used for prediction was at least one month past the March 19 inflection point obtained from the entire epidemic period. This analysis highlights the difficulty in identifying an optimal inflection point for prediction purposes during an ongoing epidemic when only a partial cumulative case number is available.</p><table-wrap id="T-2-1" position="float"><label>Table</label><caption><title>Predicted inflection point and dates when inflection occurs based on truncated data of cumulative number of reported severe acute respiratory syndrome cases in Hong Kong</title></caption><table frame="hsides" rules="groups"><col width="183" span="1"></col><col width="49" span="1"></col><col width="123" span="1"></col><col width="40" span="1"></col><col width="31" span="1"></col><col width="40" span="1"></col><thead><tr><th valign="bottom" align="left" scope="col" rowspan="1" colspan="1">Data period (ending date)</th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1"><italic>t<sub>m</sub></italic><sup>a</sup></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">Date<sup>b</sup></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1"><italic>K</italic><sup>c</sup></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1"><italic>r</italic><sup>d</sup></th><th valign="bottom" align="center" scope="col" rowspan="1" colspan="1">α<sup>e</sup></th></tr></thead><tbody><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">April 9, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">16.62</td><td valign="top" align="center" rowspan="1" colspan="1">April 2, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">1,107</td><td valign="top" align="center" rowspan="1" colspan="1">0.20</td><td valign="top" align="center" rowspan="1" colspan="1">0.74</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">April 16, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">–40.79</td><td valign="top" align="center" rowspan="1" colspan="1">February 7, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">1,907</td><td valign="top" align="center" rowspan="1" colspan="1">0.07</td><td valign="top" align="center" rowspan="1" colspan="1">52.11</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">April 30, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">–13.52</td><td valign="top" align="center" rowspan="1" colspan="1">March 3, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">1,819</td><td valign="top" align="center" rowspan="1" colspan="1">0.07</td><td valign="top" align="center" rowspan="1" colspan="1">10.21</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">May 14, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">6.80</td><td valign="top" align="center" rowspan="1" colspan="1">March 23, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">1,749</td><td valign="top" align="center" rowspan="1" colspan="1">0.09</td><td valign="top" align="center" rowspan="1" colspan="1">2.84</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">May 28, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">17.31</td><td valign="top" align="center" rowspan="1" colspan="1">April 2, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">1,733</td><td valign="top" align="center" rowspan="1" colspan="1">0.10</td><td valign="top" align="center" rowspan="1" colspan="1">1.38</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">June 12, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">2.63</td><td valign="top" align="center" rowspan="1" colspan="1">March 19, 2003</td><td valign="top" align="center" rowspan="1" colspan="1">1,751</td><td valign="top" align="center" rowspan="1" colspan="1">0.09</td><td valign="top" align="center" rowspan="1" colspan="1">3.77</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup><italic>t<sub>m</sub></italic> is the inflection point of the model.
<sup>b</sup>Date refers to the date when inflection occurs.
<sup>c</sup><italic>K</italic> is the predicted maximum number of cumulative cases.
<sup>d</sup><italic>r</italic> is the intrinsic growth rate.
<sup>e</sup>α measures the extent of deviation of S-shaped dynamics from the classic logistic growth curve.</p></table-wrap-foot></table-wrap><p>We fully agree with Hsieh et al. (<xref ref-type="bibr" rid="R-2-4"><italic>4</italic></xref>) that the quantitative assessment of the effectiveness of public health intervention measures for SARS is a difficult task for modelers. To make models useful for assessing the effects of specific intervention measures and for predicting the future dynamics during an ongoing epidemic, we need improved knowledge on the transmission mechanisms, pathogenesis, and the epidemiologic determinants of the spread of the virus. Any retrospective analysis of the 2003 SARS epidemic that improves our knowledge of SARS epidemiology is welcome.</p></body><back><fn-group><fn fn-type="citation"><p><italic>Suggested citation for this article</italic>: Zhou G, Yan G. Response to: SARS epidemiology modeling. Emerg Infect Dis [serial on the Internet]. 2004 Jun [date cited]. <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.3201/eid1006.040173">http://dx.doi.org/10.3201/eid1006.040173</ext-link></p></fn></fn-group><ref-list><title>References</title><ref id="R-2-1"><label>1. </label><mixed-citation publication-type="journal"><string-name><surname>Zhou</surname><given-names>G</given-names></string-name>, <string-name><surname>Yan</surname><given-names>G</given-names></string-name><article-title>Severe acute respiratory syndrome epidemic in Asia.</article-title><source>Emerg Infect Dis</source>. <year>2003</year>;<volume>9</volume>:<fpage>1608</fpage>–<lpage>10</lpage><pub-id pub-id-type="pmid">14720403</pub-id></mixed-citation></ref><ref id="R-2-2"><label>2. </label><mixed-citation publication-type="journal"><string-name><surname>Lipsitch</surname><given-names>M</given-names></string-name>, <string-name><surname>Cohen</surname><given-names>T</given-names></string-name>, <string-name><surname>Cooper</surname><given-names>B</given-names></string-name>, <string-name><surname>Robins</surname><given-names>JM</given-names></string-name>, <string-name><surname>Ma</surname><given-names>S</given-names></string-name>, <string-name><surname>James</surname><given-names>L</given-names></string-name>, <etal></etal><article-title>Transmission dynamics and control of severe acute respiratory syndrome.</article-title><source>Science</source>. <year>2003</year>;<volume>300</volume>:<fpage>1966</fpage>–<lpage>70</lpage><pub-id pub-id-type="doi">10.1126/science.1086616</pub-id><pub-id pub-id-type="pmid">12766207</pub-id></mixed-citation></ref><ref id="R-2-3"><label>3. </label><mixed-citation publication-type="journal"><string-name><surname>Riley</surname><given-names>S</given-names></string-name>, <string-name><surname>Fraser</surname><given-names>C</given-names></string-name>, <string-name><surname>Donnelly</surname><given-names>CA</given-names></string-name>, <string-name><surname>Ghani</surname><given-names>AC</given-names></string-name>, <string-name><surname>Abu-Raddad</surname><given-names>LJ</given-names></string-name>, <string-name><surname>Hedley</surname><given-names>AJ</given-names></string-name>, <etal></etal><article-title>Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions.</article-title><source>Science</source>. <year>2003</year>;<volume>300</volume>:<fpage>1961</fpage>–<lpage>6</lpage><pub-id pub-id-type="doi">10.1126/science.1086478</pub-id><pub-id pub-id-type="pmid">12766206</pub-id></mixed-citation></ref><ref id="R-2-4"><label>4. </label><mixed-citation publication-type="journal"><string-name><surname>Hsieh</surname><given-names>YH</given-names></string-name>, <string-name><surname>Lee</surname><given-names>JY</given-names></string-name>, <string-name><surname>Chang</surname><given-names>HL</given-names></string-name><article-title>SARS epidemiology and cumulative case curve.</article-title><source>Emerg Infect Dis</source>. <year>2004</year>;<volume>10</volume>:<fpage>1165</fpage>–<lpage>7</lpage><pub-id pub-id-type="doi">10.3201/eid1006.031023</pub-id><pub-id pub-id-type="pmid">15224675</pub-id></mixed-citation></ref></ref-list></back></response></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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