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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Using models to identify routes of nosocomial infection: a large hospital outbreak of SARS in Hong Kong</title>
<author><name sortKey="Kwok, Kin On" sort="Kwok, Kin On" uniqKey="Kwok K" first="Kin On" last="Kwok">Kin On Kwok</name>
<affiliation><nlm:aff id="aff1"><institution>Department of Community Medicine and School of Public Health, The University of Hong Kong</institution>
<addr-line>5/F 21 Sassoon Road, Pokfulam, Hong Kong SAR, China</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Leung, Gabriel M" sort="Leung, Gabriel M" uniqKey="Leung G" first="Gabriel M" last="Leung">Gabriel M. Leung</name>
<affiliation><nlm:aff id="aff1"><institution>Department of Community Medicine and School of Public Health, The University of Hong Kong</institution>
<addr-line>5/F 21 Sassoon Road, Pokfulam, Hong Kong SAR, China</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Lam, Wai Yee" sort="Lam, Wai Yee" uniqKey="Lam W" first="Wai Yee" last="Lam">Wai Yee Lam</name>
<affiliation><nlm:aff id="aff2"><institution>Department of Infectious Disease Epidemiology, Imperial College London</institution>
<addr-line>Saint Mary's Campus, Norfolk Place, London W2 1PG, UK</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Riley, Steven" sort="Riley, Steven" uniqKey="Riley S" first="Steven" last="Riley">Steven Riley</name>
<affiliation><nlm:aff id="aff1"><institution>Department of Community Medicine and School of Public Health, The University of Hong Kong</institution>
<addr-line>5/F 21 Sassoon Road, Pokfulam, Hong Kong SAR, China</addr-line>
</nlm:aff>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PMC</idno>
<idno type="pmid">17254984</idno>
<idno type="pmc">2197207</idno>
<idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2197207</idno>
<idno type="RBID">PMC:2197207</idno>
<idno type="doi">10.1098/rspb.2006.0026</idno>
<date when="2006">2006</date>
<idno type="wicri:Area/Pmc/Corpus">000D12</idno>
<idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000D12</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Using models to identify routes of nosocomial infection: a large hospital outbreak of SARS in Hong Kong</title>
<author><name sortKey="Kwok, Kin On" sort="Kwok, Kin On" uniqKey="Kwok K" first="Kin On" last="Kwok">Kin On Kwok</name>
<affiliation><nlm:aff id="aff1"><institution>Department of Community Medicine and School of Public Health, The University of Hong Kong</institution>
<addr-line>5/F 21 Sassoon Road, Pokfulam, Hong Kong SAR, China</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Leung, Gabriel M" sort="Leung, Gabriel M" uniqKey="Leung G" first="Gabriel M" last="Leung">Gabriel M. Leung</name>
<affiliation><nlm:aff id="aff1"><institution>Department of Community Medicine and School of Public Health, The University of Hong Kong</institution>
<addr-line>5/F 21 Sassoon Road, Pokfulam, Hong Kong SAR, China</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Lam, Wai Yee" sort="Lam, Wai Yee" uniqKey="Lam W" first="Wai Yee" last="Lam">Wai Yee Lam</name>
<affiliation><nlm:aff id="aff2"><institution>Department of Infectious Disease Epidemiology, Imperial College London</institution>
<addr-line>Saint Mary's Campus, Norfolk Place, London W2 1PG, UK</addr-line>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Riley, Steven" sort="Riley, Steven" uniqKey="Riley S" first="Steven" last="Riley">Steven Riley</name>
<affiliation><nlm:aff id="aff1"><institution>Department of Community Medicine and School of Public Health, The University of Hong Kong</institution>
<addr-line>5/F 21 Sassoon Road, Pokfulam, Hong Kong SAR, China</addr-line>
</nlm:aff>
</affiliation>
</author>
</analytic>
<series><title level="j">Proceedings of the Royal Society B: Biological Sciences</title>
<idno type="ISSN">0962-8452</idno>
<idno type="eISSN">1471-2954</idno>
<imprint><date when="2006">2006</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><p>Two factors dominated the epidemiology of severe acute respiratory syndrome (SARS) during the 2002–2003 global outbreak, namely super-spreading events (SSE) and hospital infections. Although both factors were important during the first and the largest hospital outbreak in Hong Kong, the relative importance of different routes of infection has not yet been quantified. We estimated the parameters of a novel mathematical model of hospital infection using SARS episode data. These estimates described levels of transmission between the index super-spreader, staff and patients, and were used to compare three plausible hypotheses. The broadest of the supported hypotheses ascribes the initial surge in cases to a single super-spreading individual and suggests that the <italic>per capita</italic>
risk of infection to patients increased approximately one month after the start of the outbreak. Our estimate for the number of cases caused by the SSE is substantially lower than the previously reported values, which were mostly based on self-reported exposure information. This discrepancy suggests that the early identification of the index case as a super-spreader might have led to biased contact tracing, resulting in too few cases being attributed to staff-to-staff transmission. We propose that in future outbreaks of SARS or other directly transmissible respiratory pathogens, simple mathematical models could be used to validate preliminary conclusions concerning the relative importance of different routes of transmission with important implications for infection control.</p>
</div>
</front>
</TEI>
<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Proc Biol Sci</journal-id>
<journal-id journal-id-type="publisher-id">RSPB</journal-id>
<journal-title>Proceedings of the Royal Society B: Biological Sciences</journal-title>
<issn pub-type="ppub">0962-8452</issn>
<issn pub-type="epub">1471-2954</issn>
<publisher><publisher-name>The Royal Society</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">17254984</article-id>
<article-id pub-id-type="pmc">2197207</article-id>
<article-id pub-id-type="publisher-id">rspb20060026</article-id>
<article-id pub-id-type="doi">10.1098/rspb.2006.0026</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Using models to identify routes of nosocomial infection: a large hospital outbreak of SARS in Hong Kong</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Kwok</surname>
<given-names>Kin On</given-names>
</name>
<xref ref-type="aff" rid="aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Leung</surname>
<given-names>Gabriel M</given-names>
</name>
<xref ref-type="aff" rid="aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Lam</surname>
<given-names>Wai Yee</given-names>
</name>
<xref ref-type="aff" rid="aff2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Riley</surname>
<given-names>Steven</given-names>
</name>
<xref ref-type="aff" rid="aff1">1</xref>
<xref ref-type="corresp" rid="cor1">*</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<institution>Department of Community Medicine and School of Public Health, The University of Hong Kong</institution>
<addr-line>5/F 21 Sassoon Road, Pokfulam, Hong Kong SAR, China</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<institution>Department of Infectious Disease Epidemiology, Imperial College London</institution>
<addr-line>Saint Mary's Campus, Norfolk Place, London W2 1PG, UK</addr-line>
</aff>
<author-notes><corresp id="cor1"><label>*</label>
Author for correspondence (<email>steven.riley@hku.hk</email>
)</corresp>
</author-notes>
<pub-date pub-type="epub"><day>12</day>
<month>12</month>
<year>2006</year>
</pub-date>
<pub-date pub-type="ppub"><day>07</day>
<month>3</month>
<year>2007</year>
</pub-date>
<volume>274</volume>
<issue>1610</issue>
<fpage>611</fpage>
<lpage>618</lpage>
<history><date date-type="received"><day>21</day>
<month>9</month>
<year>2006</year>
</date>
<date date-type="accepted"><day>8</day>
<month>11</month>
<year>2006</year>
</date>
</history>
<permissions><copyright-statement>© 2006 The Royal Society</copyright-statement>
<copyright-year>2006</copyright-year>
</permissions>
<abstract><p>Two factors dominated the epidemiology of severe acute respiratory syndrome (SARS) during the 2002–2003 global outbreak, namely super-spreading events (SSE) and hospital infections. Although both factors were important during the first and the largest hospital outbreak in Hong Kong, the relative importance of different routes of infection has not yet been quantified. We estimated the parameters of a novel mathematical model of hospital infection using SARS episode data. These estimates described levels of transmission between the index super-spreader, staff and patients, and were used to compare three plausible hypotheses. The broadest of the supported hypotheses ascribes the initial surge in cases to a single super-spreading individual and suggests that the <italic>per capita</italic>
risk of infection to patients increased approximately one month after the start of the outbreak. Our estimate for the number of cases caused by the SSE is substantially lower than the previously reported values, which were mostly based on self-reported exposure information. This discrepancy suggests that the early identification of the index case as a super-spreader might have led to biased contact tracing, resulting in too few cases being attributed to staff-to-staff transmission. We propose that in future outbreaks of SARS or other directly transmissible respiratory pathogens, simple mathematical models could be used to validate preliminary conclusions concerning the relative importance of different routes of transmission with important implications for infection control.</p>
</abstract>
<kwd-group><kwd>severe acute respiratory syndrome</kwd>
<kwd>mathematical modelling</kwd>
<kwd>nosocomial</kwd>
</kwd-group>
<kwd-group kwd-group-type="abbr"><kwd>SARS, severe acute respiratory syndrome</kwd>
<kwd>SSE, super-spreading events</kwd>
</kwd-group>
</article-meta>
</front>
<floats-wrap><fig id="fig1" position="float"><label>Figure 1</label>
<caption><p>Model structure and inputs. (<italic>a</italic>
) The structure of the dynamic model. (<italic>b</italic>
) The time-series of SARS admissions on days since the first admission. These cases are those which were known to have been community acquired and those for which no source can be reliably identified. See main text and electronic supplementary material.</p>
</caption>
<graphic xlink:href="rspb20060026f01"></graphic>
</fig>
<fig id="fig2" position="float"><label>Figure 2</label>
<caption><p>Comparison of model output and data for three different hypotheses. Hypothesis <bold>H</bold>
<sub>1</sub>
is that the initial surge in cases was caused by poor infection control and that the outbreak was controlled by a reduction in the susceptibility of staff and patients at some time <italic>t</italic>
<sub>I</sub>
. Hypothesis <bold>H</bold>
<sub>2</sub>
is that conditions for infection were constant over the duration of the outbreak and that the initial surge in cases was caused by a super-spreader. Hypothesis <bold>H</bold>
<sub>3</sub>
is that the surge in cases at the beginning was caused by a super-spreading individual and that conditions for infection also changed at some later time in the outbreak (i.e. combination of <bold>H</bold>
<sub><bold>1</bold>
</sub>
and <bold>H</bold>
<sub>2</sub>
). (<italic>a</italic>
) Staff infections (<italic>n</italic>
=164) and (<italic>b</italic>
) patient infections (<italic>n</italic>
=59).</p>
</caption>
<graphic xlink:href="rspb20060026f02"></graphic>
</fig>
<fig id="fig3" position="float"><label>Figure 3</label>
<caption><p>Sources of infection for the three different hypotheses. (<italic>a</italic>
) shows the breakdown of infections, by route of infection, for hypothesis <bold>H</bold>
<sub><bold>1</bold>
</sub>
that the initial surge in cases was caused by poor infection control and that the outbreak was controlled by a reduction in the susceptibility of staff and patients at some time <italic>t</italic>
<sub>I</sub>
. (<italic>b</italic>
) shows the same for <bold>H</bold>
<sub>2</sub>
that conditions for infection were constant over the duration of the outbreak and that the initial surge in cases was caused by the super-spreader. (<italic>c</italic>
) shows the same for <bold>H</bold>
<sub>3</sub>
that the surge in cases at the beginning was caused by a super-spreading individual and that conditions for infection also changed at some later time in the outbreak (the combination of <bold>H</bold>
<sub><bold>1</bold>
</sub>
and <bold>H</bold>
<sub>2</sub>
).</p>
</caption>
<graphic xlink:href="rspb20060026f03"></graphic>
</fig>
<table-wrap id="tbl1" position="float"><label>Table 1</label>
<caption><p>Population sizes and waiting times.</p>
</caption>
<table frame="hsides" rules="groups"><tr><th colspan="2" align="left" rowspan="1">parameter</th>
<th align="left" rowspan="1" colspan="1">value</th>
<th align="left" rowspan="1" colspan="1">notes</th>
</tr>
<tr><td rowspan="1" colspan="1"><italic>N</italic>
<sub>S</sub>
</td>
<td rowspan="1" colspan="1">total number of staff at risk of infection</td>
<td rowspan="1" colspan="1">2250</td>
<td rowspan="1" colspan="1">Total number of staff at the hospital used for the main runs. Sensitivity analyses were performed with smaller numbers (see electronic supplementary material).</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>N</italic>
<sub>P</sub>
</td>
<td rowspan="1" colspan="1">total number of patients at risk of infection</td>
<td rowspan="1" colspan="1">1315</td>
<td rowspan="1" colspan="1">Total number of hospital beds used for the main runs. Sensitivity analyses were performed with smaller numbers (see electronic supplementary material).</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>τ</italic>
<sub>E</sub>
</td>
<td rowspan="1" colspan="1">average time from infection to onset of symptoms</td>
<td rowspan="1" colspan="1">4.6 days</td>
<td rowspan="1" colspan="1">Estimated from Hong Kong SARS integrated database.</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>n</italic>
<sub><italic>Γ</italic>
</sub>
</td>
<td rowspan="1" colspan="1">shape parameter for distribution of times from infection to onset of symptoms</td>
<td rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1">Estimated from Hong Kong SARS integrated database.</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>τ</italic>
<sub>SW</sub>
</td>
<td rowspan="1" colspan="1">average time that symptomatic staff continued to work after onset before admission</td>
<td rowspan="1" colspan="1">3.3 days</td>
<td rowspan="1" colspan="1">Calculated from Hong Kong SARS integrated database for Hospital P cases.</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>τ</italic>
<sub>G</sub>
</td>
<td rowspan="1" colspan="1">generation time or serial interval; average time from the infection of an infector to the times of infection of all her infectees</td>
<td rowspan="1" colspan="1">8.4 days</td>
<td rowspan="1" colspan="1"><xref ref-type="bibr" rid="bib12">Lipsitch <italic>et al</italic>
. 2003</xref>
</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>τ</italic>
<sub>DS</sub>
</td>
<td rowspan="1" colspan="1">average duration from onset to discharge from the hospital</td>
<td rowspan="1" colspan="1">26.5 days</td>
<td rowspan="1" colspan="1">Calculated from Hong Kong SARS integrated database.</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>τ</italic>
<sub>OA</sub>
</td>
<td rowspan="1" colspan="1">average time from onset of symptoms to admission for patients infected outside the hospital</td>
<td rowspan="1" colspan="1">3.98 days</td>
<td rowspan="1" colspan="1">Calculated from Hong Kong SARS integrated database for Hospital P cases.</td>
</tr>
</table>
</table-wrap>
<table-wrap id="tbl2" position="float"><label>Table 2</label>
<caption><p>Estimated parameters. Values were obtained using approximate maximum-likelihood methods. We assumed that data arose from independent Poisson distributions for each type of infection for each day. The means of these distributions were the average daily model incidences, calculated using a deterministic version of the model. The use of this approximate likelihood function was validated by ensuring that unbiased estimates of model parameters could be recovered from data simulated with a (compartmental) stochastic version of the model. The number of parameters used to calculate the ΔAIC was the same as the number reported in this table, i.e. 6 for <bold>H</bold>
<sub><bold>1</bold>
</sub>
, 5 for <bold>H</bold>
<sub>2</sub>
and 8 for <bold>H</bold>
<sub>3</sub>
. As the ratio of parameters to data points was relatively high, we used the following formula: <inline-formula><inline-graphic xlink:href="rspb20060026e05.jpg" alternate-form-of="M5"></inline-graphic>
<mml:math id="M5"><mml:mrow><mml:mtext>AIC</mml:mtext>
<mml:mo>=</mml:mo>
<mml:mo>−</mml:mo>
<mml:mn>2</mml:mn>
<mml:mi>l</mml:mi>
<mml:mo>+</mml:mo>
<mml:mn>2</mml:mn>
<mml:mi>K</mml:mi>
<mml:mo>+</mml:mo>
<mml:mn>2</mml:mn>
<mml:mi>K</mml:mi>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>K</mml:mi>
<mml:mo>+</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo stretchy="false">)</mml:mo>
<mml:mo>/</mml:mo>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>n</mml:mi>
<mml:mo>−</mml:mo>
<mml:mi>K</mml:mi>
<mml:mo>−</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula>
, where <italic>l</italic>
was the log likelihood; <italic>K</italic>
was the number of parameters; and <italic>n</italic>
was the size of the dataset. The intervals given are based on the univariate likelihood profile. Note that, although the model was solved using a time-step of 0.1 days, we considered only integer-valued days for the duration of infectiousness of the super-spreader and for the times of intervention. In addition, due to the long time gap before the last two cases (see <xref ref-type="fig" rid="fig1">figure 1</xref>
<italic>a</italic>
), we estimated parameter values using data only for the first 82 days of the outbreak.</p>
</caption>
<table frame="hsides" rules="groups"><tr><th colspan="2" align="left" rowspan="1">parameter</th>
<th rowspan="1" colspan="1"><bold>H</bold>
<sub>1</sub>
interventions only</th>
<th rowspan="1" colspan="1"><bold>H</bold>
<sub>2</sub>
super-spreader only</th>
<th rowspan="1" colspan="1"><bold>H</bold>
<sub>3</sub>
super-spreader and interventions</th>
</tr>
<tr><td rowspan="1" colspan="1"><inline-formula><inline-graphic xlink:href="rspb20060026e06.jpg" alternate-form-of="M6"></inline-graphic>
<mml:math id="M6"><mml:mrow><mml:msubsup><mml:mi>R</mml:mi>
<mml:mn>0</mml:mn>
<mml:mrow><mml:mtext>XSS</mml:mtext>
</mml:mrow>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</td>
<td rowspan="1" colspan="1">basic reproductive number, excluding super-spreaders</td>
<td align="left" rowspan="1" colspan="1">174 (167, 181)</td>
<td align="left" rowspan="1" colspan="1">0.660 (0.624, 0.695)</td>
<td align="left" rowspan="1" colspan="1">0.595 (0.573, 0.617)</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>α</italic>
<sub>SSP</sub>
</td>
<td rowspan="1" colspan="1">infectivity of super-spreading patient, relative to that of staff</td>
<td align="left" rowspan="1" colspan="1">—</td>
<td align="left" rowspan="1" colspan="1">76.8 (65.5, 89.2)</td>
<td align="left" rowspan="1" colspan="1">48.6 (42.1, 55.8)</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>t</italic>
<sub>SSP</sub>
</td>
<td rowspan="1" colspan="1">duration of infectivity of super-spreading patient (days)</td>
<td align="left" rowspan="1" colspan="1">—</td>
<td align="left" rowspan="1" colspan="1">4.0 (3.6, 4.6)</td>
<td align="left" rowspan="1" colspan="1">4 (3.6, 4.5)</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>α</italic>
<sub>P</sub>
</td>
<td rowspan="1" colspan="1">infectivity of non-super-spreading patients relative to that of staff</td>
<td align="left" rowspan="1" colspan="1">0.0852 (0.0695, 0.106)</td>
<td align="left" rowspan="1" colspan="1">0.112 (0.058, 0.203)</td>
<td align="left" rowspan="1" colspan="1">0 (0, 0.00073)</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>γ</italic>
<sub>P</sub>
(0)</td>
<td rowspan="1" colspan="1">susceptibility of non-super-spreading patients at time <italic>t</italic>
=0, relative to that of staff at time <italic>t</italic>
=0</td>
<td align="left" rowspan="1" colspan="1">0.302 (0.231, 0.388)</td>
<td align="left" rowspan="1" colspan="1">0.645 (0.482, 0.851)</td>
<td align="left" rowspan="1" colspan="1">0.382 (0.291, 0.492)</td>
</tr>
<tr><td rowspan="1" colspan="1"><italic>t</italic>
<sub>I</sub>
</td>
<td rowspan="1" colspan="1">time of intervention (days)</td>
<td align="left" rowspan="1" colspan="1">3.0 (2.9, 3.1)</td>
<td align="left" rowspan="1" colspan="1">—</td>
<td align="left" rowspan="1" colspan="1">25 (22.2, 27.8)</td>
</tr>
<tr><td rowspan="1" colspan="1"><inline-formula><inline-graphic xlink:href="rspb20060026e07.jpg" alternate-form-of="M7"></inline-graphic>
<mml:math id="M7"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">Δ</mml:mi>
<mml:mtext>P</mml:mtext>
<mml:mi>γ</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</td>
<td rowspan="1" colspan="1">susceptibility of non-super-spreading patients after time <italic>t</italic>
><italic>t</italic>
<sub>I</sub>
, relative to that of staff at time <italic>t</italic>
=0</td>
<td align="left" rowspan="1" colspan="1">0.010 (0.007, 0.013)</td>
<td align="left" rowspan="1" colspan="1">—</td>
<td align="left" rowspan="1" colspan="1">4.99 (3.42, 6.98)</td>
</tr>
<tr><td rowspan="1" colspan="1"><inline-formula><inline-graphic xlink:href="rspb20060026e08.jpg" alternate-form-of="M8"></inline-graphic>
<mml:math id="M8"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">Δ</mml:mi>
<mml:mtext>S</mml:mtext>
<mml:mi>γ</mml:mi>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</td>
<td rowspan="1" colspan="1">susceptibility of staff after time <italic>t</italic>
><italic>t</italic>
<sub>I</sub>
, relative to that of staff at time <italic>t</italic>
=0</td>
<td align="left" rowspan="1" colspan="1">0.0034 (0.0031, 0.0038)</td>
<td align="left" rowspan="1" colspan="1">—</td>
<td align="left" rowspan="1" colspan="1">1.29 (1.17, 1.41)</td>
</tr>
<tr><td rowspan="1" colspan="1">ΔAIC</td>
<td align="left" rowspan="1" colspan="1">0</td>
<td align="left" rowspan="1" colspan="1">14.3</td>
<td align="left" rowspan="1" colspan="1">2.21</td>
</tr>
</table>
</table-wrap>
<table-wrap id="tbl3" position="float"><label>Table 3</label>
<caption><p>Estimated numbers of infections by different transmission sources for the three different hypotheses (see text and <xref ref-type="fig" rid="fig2">figure 2</xref>
for definitions of these hypotheses).</p>
</caption>
<table frame="hsides" rules="groups"><tr><th rowspan="1" colspan="1"> </th>
<th colspan="6" align="left" rowspan="1">infectee</th>
</tr>
<tr><th rowspan="1" colspan="1"> </th>
<th colspan="3" align="left" rowspan="1">staff</th>
<th colspan="3" align="left" rowspan="1">patients</th>
</tr>
<tr><th align="left" rowspan="1" colspan="1">infector</th>
<th rowspan="1" colspan="1"><bold>H</bold>
<sub>1</sub>
</th>
<th rowspan="1" colspan="1"><bold>H</bold>
<sub>2</sub>
</th>
<th rowspan="1" colspan="1"><bold>H</bold>
<sub>3</sub>
</th>
<th rowspan="1" colspan="1"><bold>H</bold>
<sub>1</sub>
</th>
<th rowspan="1" colspan="1"><bold>H</bold>
<sub>2</sub>
</th>
<th rowspan="1" colspan="1"><bold>H</bold>
<sub>3</sub>
</th>
</tr>
<tr><td rowspan="1" colspan="1">super-spreader</td>
<td align="char" rowspan="1" colspan="1">—</td>
<td align="char" rowspan="1" colspan="1">56.0</td>
<td align="char" rowspan="1" colspan="1">59.4</td>
<td align="char" rowspan="1" colspan="1">—</td>
<td align="char" rowspan="1" colspan="1">21.2</td>
<td align="char" rowspan="1" colspan="1">13.4</td>
</tr>
<tr><td rowspan="1" colspan="1">‘other’ patients</td>
<td align="char" rowspan="1" colspan="1">58.2</td>
<td align="char" rowspan="1" colspan="1">54.8</td>
<td align="char" rowspan="1" colspan="1">0</td>
<td align="char" rowspan="1" colspan="1">23.7</td>
<td align="char" rowspan="1" colspan="1">19.0</td>
<td align="char" rowspan="1" colspan="1">0</td>
</tr>
<tr><td rowspan="1" colspan="1">staff</td>
<td align="char" rowspan="1" colspan="1">110.3</td>
<td align="char" rowspan="1" colspan="1">57.5</td>
<td align="char" rowspan="1" colspan="1">107.3</td>
<td align="char" rowspan="1" colspan="1">37.2</td>
<td align="char" rowspan="1" colspan="1">20.2</td>
<td align="char" rowspan="1" colspan="1">47.5</td>
</tr>
<tr><td rowspan="1" colspan="1">total</td>
<td align="char" rowspan="1" colspan="1">168.5</td>
<td align="char" rowspan="1" colspan="1">168.3</td>
<td align="char" rowspan="1" colspan="1">166.7</td>
<td align="char" rowspan="1" colspan="1">60.9</td>
<td align="char" rowspan="1" colspan="1">60.4</td>
<td align="char" rowspan="1" colspan="1">60.9</td>
</tr>
<tr><td rowspan="1" colspan="1">data</td>
<td align="char" rowspan="1" colspan="1"></td>
<td align="char" rowspan="1" colspan="1">164</td>
<td align="char" rowspan="1" colspan="1"></td>
<td align="char" rowspan="1" colspan="1"></td>
<td align="char" rowspan="1" colspan="1">59</td>
<td align="char" rowspan="1" colspan="1"></td>
</tr>
</table>
</table-wrap>
</floats-wrap>
</pmc>
</record>
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