Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China
Identifieur interne : 000048 ( Pmc/Checkpoint ); précédent : 000047; suivant : 000049Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China
Auteurs : Péter Boldog ; Tamás Tekeli ; Zsolt Vizi ; Attila Dénes ; Ferenc A. Bartha ; Gergely RöstSource :
- Journal of Clinical Medicine [ 2077-0383 ] ; 2020.
Abstract
We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the cumulative number of cases in mainland China outside the closed areas; (ii) the connectivity of the destination country with China, including baseline travel frequencies, the effect of travel restrictions, and the efficacy of entry screening at destination; and (iii) the efficacy of control measures in the destination country (expressed by the local reproduction number
Url:
DOI: 10.3390/jcm9020571
PubMed: 32093043
PubMed Central: 7073711
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China</title><author><name sortKey="Boldog, Peter" sort="Boldog, Peter" uniqKey="Boldog P" first="Péter" last="Boldog">Péter Boldog</name></author><author><name sortKey="Tekeli, Tamas" sort="Tekeli, Tamas" uniqKey="Tekeli T" first="Tamás" last="Tekeli">Tamás Tekeli</name></author><author><name sortKey="Vizi, Zsolt" sort="Vizi, Zsolt" uniqKey="Vizi Z" first="Zsolt" last="Vizi">Zsolt Vizi</name></author><author><name sortKey="Denes, Attila" sort="Denes, Attila" uniqKey="Denes A" first="Attila" last="Dénes">Attila Dénes</name></author><author><name sortKey="Bartha, Ferenc A" sort="Bartha, Ferenc A" uniqKey="Bartha F" first="Ferenc A." last="Bartha">Ferenc A. Bartha</name></author><author><name sortKey="Rost, Gergely" sort="Rost, Gergely" uniqKey="Rost G" first="Gergely" last="Röst">Gergely Röst</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32093043</idno><idno type="pmc">7073711</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073711</idno><idno type="RBID">PMC:7073711</idno><idno type="doi">10.3390/jcm9020571</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000912</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000912</idno><idno type="wicri:Area/Pmc/Curation">000912</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000912</idno><idno type="wicri:Area/Pmc/Checkpoint">000048</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000048</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China</title><author><name sortKey="Boldog, Peter" sort="Boldog, Peter" uniqKey="Boldog P" first="Péter" last="Boldog">Péter Boldog</name></author><author><name sortKey="Tekeli, Tamas" sort="Tekeli, Tamas" uniqKey="Tekeli T" first="Tamás" last="Tekeli">Tamás Tekeli</name></author><author><name sortKey="Vizi, Zsolt" sort="Vizi, Zsolt" uniqKey="Vizi Z" first="Zsolt" last="Vizi">Zsolt Vizi</name></author><author><name sortKey="Denes, Attila" sort="Denes, Attila" uniqKey="Denes A" first="Attila" last="Dénes">Attila Dénes</name></author><author><name sortKey="Bartha, Ferenc A" sort="Bartha, Ferenc A" uniqKey="Bartha F" first="Ferenc A." last="Bartha">Ferenc A. Bartha</name></author><author><name sortKey="Rost, Gergely" sort="Rost, Gergely" uniqKey="Rost G" first="Gergely" last="Röst">Gergely Röst</name></author></analytic><series><title level="j">Journal of Clinical Medicine</title><idno type="eISSN">2077-0383</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the cumulative number of cases in mainland China outside the closed areas; (ii) the connectivity of the destination country with China, including baseline travel frequencies, the effect of travel restrictions, and the efficacy of entry screening at destination; and (iii) the efficacy of control measures in the destination country (expressed by the local reproduction number <inline-formula><mml:math id="mm1"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>). We found that in countries with low connectivity to China but with relatively high <inline-formula><mml:math id="mm2"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, the most beneficial control measure to reduce the risk of outbreaks is a further reduction in their importation number either by entry screening or travel restrictions. Countries with high connectivity but low <inline-formula><mml:math id="mm3"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> benefit the most from policies that further reduce <inline-formula><mml:math id="mm4"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>. Countries in the middle should consider a combination of such policies. Risk assessments were illustrated for selected groups of countries from America, Asia, and Europe. We investigated how their risks depend on those parameters, and how the risk is increasing in time as the number of cases in China is growing.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Huang, C" uniqKey="Huang C">C. Huang</name></author><author><name sortKey="Wang, Y" uniqKey="Wang Y">Y. Wang</name></author><author><name sortKey="Li, X" uniqKey="Li X">X. Li</name></author><author><name sortKey="Ren, L" uniqKey="Ren L">L. Ren</name></author><author><name sortKey="Zhao, J" uniqKey="Zhao J">J. Zhao</name></author><author><name sortKey="Hu, Y" uniqKey="Hu Y">Y. Hu</name></author><author><name sortKey="Zhang, L" uniqKey="Zhang L">L. Zhang</name></author><author><name sortKey="Fan, G" uniqKey="Fan G">G. Fan</name></author><author><name sortKey="Xu, J" uniqKey="Xu J">J. Xu</name></author><author><name sortKey="Gu, X" uniqKey="Gu X">X. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Clin Med</journal-id><journal-id journal-id-type="iso-abbrev">J Clin Med</journal-id><journal-id journal-id-type="publisher-id">jcm</journal-id><journal-title-group><journal-title>Journal of Clinical Medicine</journal-title></journal-title-group><issn pub-type="epub">2077-0383</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32093043</article-id><article-id pub-id-type="pmc">7073711</article-id><article-id pub-id-type="doi">10.3390/jcm9020571</article-id><article-id pub-id-type="publisher-id">jcm-09-00571</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Boldog</surname><given-names>Péter</given-names></name></contrib><contrib contrib-type="author"><name><surname>Tekeli</surname><given-names>Tamás</given-names></name></contrib><contrib contrib-type="author"><name><surname>Vizi</surname><given-names>Zsolt</given-names></name></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0003-1827-7932</contrib-id><name><surname>Dénes</surname><given-names>Attila</given-names></name><xref rid="c1-jcm-09-00571" ref-type="corresp">*</xref></contrib><contrib contrib-type="author"><name><surname>Bartha</surname><given-names>Ferenc A.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Röst</surname><given-names>Gergely</given-names></name></contrib></contrib-group><aff id="af1-jcm-09-00571">Bolyai Institute, University of Szeged, H-6720 Szeged, Hungary;<email>boldogpeter@gmail.com</email> (P.B.);<email>tekeli.tamas@gmail.com</email> (T.T.);<email>zsvizi@math.u-szeged.hu</email> (Z.V.);<email>barfer@math.u-szeged.hu</email> (F.A.B.);<email>rost@math.u-szeged.hu</email> (G.R.)</aff><author-notes><corresp id="c1-jcm-09-00571"><label>*</label>Correspondence: <email>denesa@math.u-szeged.hu</email></corresp></author-notes><pub-date pub-type="epub"><day>19</day><month>2</month><year>2020</year></pub-date><pub-date pub-type="collection"><month>2</month><year>2020</year></pub-date><volume>9</volume><issue>2</issue><elocation-id>571</elocation-id><history><date date-type="received"><day>05</day><month>2</month><year>2020</year></date><date date-type="accepted"><day>14</day><month>2</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type="open-access"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the cumulative number of cases in mainland China outside the closed areas; (ii) the connectivity of the destination country with China, including baseline travel frequencies, the effect of travel restrictions, and the efficacy of entry screening at destination; and (iii) the efficacy of control measures in the destination country (expressed by the local reproduction number <inline-formula><mml:math id="mm1"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>). We found that in countries with low connectivity to China but with relatively high <inline-formula><mml:math id="mm2"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, the most beneficial control measure to reduce the risk of outbreaks is a further reduction in their importation number either by entry screening or travel restrictions. Countries with high connectivity but low <inline-formula><mml:math id="mm3"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> benefit the most from policies that further reduce <inline-formula><mml:math id="mm4"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>. Countries in the middle should consider a combination of such policies. Risk assessments were illustrated for selected groups of countries from America, Asia, and Europe. We investigated how their risks depend on those parameters, and how the risk is increasing in time as the number of cases in China is growing.</p></abstract><kwd-group><kwd>novel coronavirus</kwd><kwd>transmission</kwd><kwd>risk assessment</kwd><kwd>interventions</kwd><kwd>travel</kwd><kwd>outbreak</kwd><kwd>COVID-19</kwd><kwd>compartmental model</kwd><kwd>branching process</kwd></kwd-group></article-meta></front><floats-group><fig id="jcm-09-00571-f001" orientation="portrait" position="float"><label>Figure 1</label><caption><p>Final epidemic sizes in China, outside Hubei, with <inline-formula><mml:math id="mm112"><mml:mrow><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mn>2.1</mml:mn><mml:mo>,</mml:mo><mml:mn>2.6</mml:mn><mml:mo>,</mml:mo><mml:mn>3.1</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula>, as a function of the time when the control function <inline-formula><mml:math id="mm113"><mml:mrow><mml:mrow><mml:mi>u</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> reaches its maximum (in days after 23 January). Rapid implementation of the control generates much smaller case numbers. The inset shows the estimations of the ascertainment rate for the week 25–31, with average <inline-formula><mml:math id="mm114"><mml:mrow><mml:mrow><mml:mn>0.063</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula>, based on the ratio of confirmed cases and the maximum likelihood estimates of the case numbers from exportation.</p></caption><graphic xlink:href="jcm-09-00571-g001"></graphic></fig><fig id="jcm-09-00571-f002" orientation="portrait" position="float"><label>Figure 2</label><caption><p>(<bold>Left</bold>) Risk of major outbreaks as a function of cumulative number of cases in selected countries, assuming <inline-formula><mml:math id="mm115"><mml:mrow><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>1.6</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula> and baseline connectivity to China. Other countries in South America, including Mexico, are inside the green shaded area. (<bold>Right</bold>) The effects of reductions of imported case numbers (either by travel restriction or entry screening) in the USA and Canada, assuming <inline-formula><mml:math id="mm116"><mml:mrow><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>1.4</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula>.</p></caption><graphic xlink:href="jcm-09-00571-g002"></graphic></fig><fig id="jcm-09-00571-f003" orientation="portrait" position="float"><label>Figure 3</label><caption><p>Outbreak risks for highly connected countries in Asia. Thailand and the Republic of Korea are plotted; the curves for Japan and Taiwan are in between them. (<bold>Left</bold>) We plot the risk vs. the efficacy of prevented importations when the cumulative number of cases reaches 150,000. (<bold>Right</bold>) <italic>C</italic> = 600,000. Black parts of the curves represent situations when the four countries are indistinguishable.</p></caption><graphic xlink:href="jcm-09-00571-g003"></graphic></fig><fig id="jcm-09-00571-f004" orientation="portrait" position="float"><label>Figure 4</label><caption><p>Selected European countries with high, medium, and low connectivity to China. (<bold>Left</bold>) The outbreak risk is plotted assuming their baseline connectivity <inline-formula><mml:math id="mm117"><mml:mrow><mml:mi>θ</mml:mi></mml:mrow></mml:math></inline-formula>, and <inline-formula><mml:math id="mm118"><mml:mrow><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>1.4</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula> for each country, as the cumulative number of cases is increasing. A significant reduction in the risks can be observed (<bold>Right</bold>), where we reduced <inline-formula><mml:math id="mm119"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> to 1.1 and assumed a 50% reduction in importations.</p></caption><graphic xlink:href="jcm-09-00571-g004"></graphic></fig><fig id="jcm-09-00571-f005" orientation="portrait" position="float"><label>Figure 5</label><caption><p>Heatmap of the outbreak risks as functions of <inline-formula><mml:math id="mm120"><mml:mrow><mml:mi>θ</mml:mi></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="mm121"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>loc</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, when <italic>C</italic> = 200,000. The arrows show the directions corresponding to the largest reductions in the risk.</p></caption><graphic xlink:href="jcm-09-00571-g005"></graphic></fig><table-wrap id="jcm-09-00571-t001" orientation="portrait" position="float"><object-id pub-id-type="pii">jcm-09-00571-t001_Table 1</object-id><label>Table 1</label><caption><p>Published estimates of the key epidemiological parameters of 2019-nCoV. Uncertainty range is given where provided.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-bottom:solid thin;border-top:solid thin" rowspan="1" colspan="1"><inline-formula><mml:math id="mm122"><mml:mrow><mml:mstyle mathvariant="bold"><mml:msub><mml:mi mathvariant="bold-italic">R</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mstyle></mml:mrow></mml:math></inline-formula></th><th align="center" valign="middle" style="border-bottom:solid thin;border-top:solid thin" rowspan="1" colspan="1">Incubation Period</th><th align="center" valign="middle" style="border-bottom:solid thin;border-top:solid thin" rowspan="1" colspan="1">Method of Estimation</th><th align="center" valign="middle" style="border-bottom:solid thin;border-top:solid thin" rowspan="1" colspan="1">Reference</th></tr></thead><tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">2.6 (1.5–3.5)</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">Epidemic Simulations</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B19-jcm-09-00571" ref-type="bibr">19</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">2.2 (1.4–3.8)</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">Stochastic Simulations</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B20-jcm-09-00571" ref-type="bibr">20</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">2.9 (2.3–3.6)</td><td align="center" valign="middle" rowspan="1" colspan="1">4.8 days</td><td align="center" valign="middle" rowspan="1" colspan="1">Exp. Growth, Max. Likelihood Est.</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B21-jcm-09-00571" ref-type="bibr">21</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">2.56 (2.49–2.63)</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">Exp. Growth, Max. Likelihood Est.</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B17-jcm-09-00571" ref-type="bibr">17</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">3.11 (2.3–4.1)</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">SEIR</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B22-jcm-09-00571" ref-type="bibr">22</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">2.5 (2.0–3.1)</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">Incidence Decay and Exponential Adjustment model</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B23-jcm-09-00571" ref-type="bibr">23</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">2.2 (1.4–3.9)</td><td align="center" valign="middle" rowspan="1" colspan="1">5.2 days (4.1–7.0)</td><td align="center" valign="middle" rowspan="1" colspan="1">Renewal Equations</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B24-jcm-09-00571" ref-type="bibr">24</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">−(1.4–4.0)</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">SEIR</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B25-jcm-09-00571" ref-type="bibr">25</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">4.71 (4.5–4.9)</td><td align="center" valign="middle" rowspan="1" colspan="1"><inline-formula><mml:math id="mm123"><mml:mrow><mml:mrow><mml:mn>5.0</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula> days (<inline-formula><mml:math id="mm124"><mml:mrow><mml:mrow><mml:mn>4.9</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula>–<inline-formula><mml:math id="mm125"><mml:mrow><mml:mrow><mml:mn>5.1</mml:mn></mml:mrow></mml:mrow></mml:math></inline-formula>)</td><td align="center" valign="middle" rowspan="1" colspan="1">Dec. 2019, SEIJR, MCMC</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B26-jcm-09-00571" ref-type="bibr">26</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">2.08 (1.9–2.2)</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">Jan. 2020, SEIJR, MCMC</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B26-jcm-09-00571" ref-type="bibr">26</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">2.68 (2.4–2.9)</td><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">SEIR, MCMC</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B27-jcm-09-00571" ref-type="bibr">27</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">5.8 days 4.6–7.9)</td><td align="center" valign="middle" rowspan="1" colspan="1">Weibull</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B28-jcm-09-00571" ref-type="bibr">28</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">4.6 days (3.3–5.8)</td><td align="center" valign="middle" rowspan="1" colspan="1">Weibull incl. Wuhan</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B29-jcm-09-00571" ref-type="bibr">29</xref>]</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">-</td><td align="center" valign="middle" rowspan="1" colspan="1">5.0 days (4.0–5.8)</td><td align="center" valign="middle" rowspan="1" colspan="1">Weibull excl. Wuhan</td><td align="center" valign="middle" rowspan="1" colspan="1">[<xref rid="B29-jcm-09-00571" ref-type="bibr">29</xref>]</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">-</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">5.1 days (4.4–6.1)</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">LogNormal</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">[<xref rid="B30-jcm-09-00571" ref-type="bibr">30</xref>]</td></tr></tbody></table></table-wrap></floats-group></pmc><affiliations><list></list><tree><noCountry><name sortKey="Bartha, Ferenc A" sort="Bartha, Ferenc A" uniqKey="Bartha F" first="Ferenc A." last="Bartha">Ferenc A. Bartha</name><name sortKey="Boldog, Peter" sort="Boldog, Peter" uniqKey="Boldog P" first="Péter" last="Boldog">Péter Boldog</name><name sortKey="Denes, Attila" sort="Denes, Attila" uniqKey="Denes A" first="Attila" last="Dénes">Attila Dénes</name><name sortKey="Rost, Gergely" sort="Rost, Gergely" uniqKey="Rost G" first="Gergely" last="Röst">Gergely Röst</name><name sortKey="Tekeli, Tamas" sort="Tekeli, Tamas" uniqKey="Tekeli T" first="Tamás" last="Tekeli">Tamás Tekeli</name><name sortKey="Vizi, Zsolt" sort="Vizi, Zsolt" uniqKey="Vizi Z" first="Zsolt" last="Vizi">Zsolt Vizi</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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