The reproductive number of COVID-19 is higher compared to SARS coronavirus
Identifieur interne : 000943 ( Pmc/Corpus ); précédent : 000942; suivant : 000944The reproductive number of COVID-19 is higher compared to SARS coronavirus
Auteurs : Ying Liu ; Albert A. Gayle ; Annelies Wilder-Smith ; Joacim RocklövSource :
- Journal of Travel Medicine [ 1195-1982 ] ; 2020.
Url:
DOI: 10.1093/jtm/taaa021
PubMed: 32052846
PubMed Central: 7074654
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">The reproductive number of COVID-19 is higher compared to SARS coronavirus</title><author><name sortKey="Liu, Ying" sort="Liu, Ying" uniqKey="Liu Y" first="Ying" last="Liu">Ying Liu</name><affiliation><nlm:aff id="aff1"><institution>School of International Business</institution>, Xiamen University Tan Kah Kee College, Zhangzhou, 363105,<country country="CN">China</country></nlm:aff></affiliation></author><author><name sortKey="Gayle, Albert A" sort="Gayle, Albert A" uniqKey="Gayle A" first="Albert A" last="Gayle">Albert A. Gayle</name><affiliation><nlm:aff id="aff2"><institution>Department of Public Health and Clinical Medicine</institution>, Section of Sustainable Health, Umeå University, SE-90187 Umeå,<country country="SE">Sweden</country></nlm:aff></affiliation></author><author><name sortKey="Wilder Smith, Annelies" sort="Wilder Smith, Annelies" uniqKey="Wilder Smith A" first="Annelies" last="Wilder-Smith">Annelies Wilder-Smith</name><affiliation><nlm:aff id="aff3"><institution>Heidelberg Institute of Public Health</institution>, Im Neuenheimer Feld 130/3, 69120 Heidelberg,<country country="DE">Germany</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff4"><institution>Department of Epidemiology and Global Health</institution>, Umeå University, SE-90187 Umeå,<country country="SE">Sweden</country></nlm:aff></affiliation></author><author><name sortKey="Rocklov, Joacim" sort="Rocklov, Joacim" uniqKey="Rocklov J" first="Joacim" last="Rocklöv">Joacim Rocklöv</name><affiliation><nlm:aff id="aff2"><institution>Department of Public Health and Clinical Medicine</institution>, Section of Sustainable Health, Umeå University, SE-90187 Umeå,<country country="SE">Sweden</country></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32052846</idno><idno type="pmc">7074654</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074654</idno><idno type="RBID">PMC:7074654</idno><idno type="doi">10.1093/jtm/taaa021</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000943</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000943</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">The reproductive number of COVID-19 is higher compared to SARS coronavirus</title><author><name sortKey="Liu, Ying" sort="Liu, Ying" uniqKey="Liu Y" first="Ying" last="Liu">Ying Liu</name><affiliation><nlm:aff id="aff1"><institution>School of International Business</institution>, Xiamen University Tan Kah Kee College, Zhangzhou, 363105,<country country="CN">China</country></nlm:aff></affiliation></author><author><name sortKey="Gayle, Albert A" sort="Gayle, Albert A" uniqKey="Gayle A" first="Albert A" last="Gayle">Albert A. Gayle</name><affiliation><nlm:aff id="aff2"><institution>Department of Public Health and Clinical Medicine</institution>, Section of Sustainable Health, Umeå University, SE-90187 Umeå,<country country="SE">Sweden</country></nlm:aff></affiliation></author><author><name sortKey="Wilder Smith, Annelies" sort="Wilder Smith, Annelies" uniqKey="Wilder Smith A" first="Annelies" last="Wilder-Smith">Annelies Wilder-Smith</name><affiliation><nlm:aff id="aff3"><institution>Heidelberg Institute of Public Health</institution>, Im Neuenheimer Feld 130/3, 69120 Heidelberg,<country country="DE">Germany</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff4"><institution>Department of Epidemiology and Global Health</institution>, Umeå University, SE-90187 Umeå,<country country="SE">Sweden</country></nlm:aff></affiliation></author><author><name sortKey="Rocklov, Joacim" sort="Rocklov, Joacim" uniqKey="Rocklov J" first="Joacim" last="Rocklöv">Joacim Rocklöv</name><affiliation><nlm:aff id="aff2"><institution>Department of Public Health and Clinical Medicine</institution>, Section of Sustainable Health, Umeå University, SE-90187 Umeå,<country country="SE">Sweden</country></nlm:aff></affiliation></author></analytic><series><title level="j">Journal of Travel Medicine</title><idno type="ISSN">1195-1982</idno><idno type="eISSN">1708-8305</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Wu, Jt" uniqKey="Wu J">JT Wu</name></author><author><name sortKey="Leung, K" uniqKey="Leung K">K Leung</name></author><author><name sortKey="Leung, Gm" uniqKey="Leung G">GM Leung</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Shen, M" uniqKey="Shen M">M Shen</name></author><author><name sortKey="Peng, Z" uniqKey="Peng Z">Z Peng</name></author><author><name sortKey="Xiao, Y" uniqKey="Xiao Y">Y Xiao</name></author><author><name sortKey="Zhang, L" uniqKey="Zhang L">L Zhang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liu, T" uniqKey="Liu T">T Liu</name></author><author><name sortKey="Hu, J" uniqKey="Hu J">J Hu</name></author><author><name sortKey="Kang, M" uniqKey="Kang M">M Kang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Read, Jm" uniqKey="Read J">JM Read</name></author><author><name sortKey="Bridgen, Jre" uniqKey="Bridgen J">JRE Bridgen</name></author><author><name sortKey="Cummings, Dat" uniqKey="Cummings D">DAT Cummings</name></author><author><name sortKey="Ho, A" uniqKey="Ho A">A Ho</name></author><author><name sortKey="Jewell, Cp" uniqKey="Jewell C">CP Jewell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Majumder, M" uniqKey="Majumder M">M Majumder</name></author><author><name sortKey="Mandl, Kd" uniqKey="Mandl K">KD. Mandl</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cao, Z" uniqKey="Cao Z">Z Cao</name></author><author><name sortKey="Zhang, Q" uniqKey="Zhang Q">Q Zhang</name></author><author><name sortKey="Lu, X" uniqKey="Lu X">X Lu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhao, S" uniqKey="Zhao S">S Zhao</name></author><author><name sortKey="Ran, J" uniqKey="Ran J">J Ran</name></author><author><name sortKey="Musa, Ss" uniqKey="Musa S">SS Musa</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Imai, N" uniqKey="Imai N">N Imai</name></author><author><name sortKey="Cori, A" uniqKey="Cori A">A Cori</name></author><author><name sortKey="Dorigatti, I" uniqKey="Dorigatti I">I Dorigatti</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Riou, J" uniqKey="Riou J">J Riou</name></author><author><name sortKey="Althaus, Cl" uniqKey="Althaus C">CL Althaus</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tang, B" uniqKey="Tang B">B Tang</name></author><author><name sortKey="Wang, X" uniqKey="Wang X">X Wang</name></author><author><name sortKey="Li, Q" uniqKey="Li Q">Q Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Qun, L" uniqKey="Qun L">L Qun</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Travel Med</journal-id><journal-id journal-id-type="iso-abbrev">J Travel Med</journal-id><journal-id journal-id-type="publisher-id">jtm</journal-id><journal-title-group><journal-title>Journal of Travel Medicine</journal-title></journal-title-group><issn pub-type="ppub">1195-1982</issn><issn pub-type="epub">1708-8305</issn><publisher><publisher-name>Oxford University Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32052846</article-id><article-id pub-id-type="pmc">7074654</article-id><article-id pub-id-type="doi">10.1093/jtm/taaa021</article-id><article-id pub-id-type="publisher-id">taaa021</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Letter</subject></subj-group></article-categories><title-group><article-title>The reproductive number of COVID-19 is higher compared to SARS coronavirus</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Liu</surname><given-names>Ying</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Gayle</surname><given-names>Albert A</given-names></name><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Wilder-Smith</surname><given-names>Annelies</given-names></name><xref ref-type="aff" rid="aff3">3</xref><xref ref-type="aff" rid="aff4">4</xref></contrib><contrib contrib-type="author"><name><surname>Rocklöv</surname><given-names>Joacim</given-names></name><xref ref-type="aff" rid="aff2">2</xref><xref rid="cor1" ref-type="corresp"></xref><pmc-comment>joacim.rocklov@umu.se </pmc-comment>
</contrib></contrib-group><aff id="aff1"><label>1</label><institution>School of International Business</institution>, Xiamen University Tan Kah Kee College, Zhangzhou, 363105,<country country="CN">China</country></aff><aff id="aff2"><label>2</label><institution>Department of Public Health and Clinical Medicine</institution>, Section of Sustainable Health, Umeå University, SE-90187 Umeå,<country country="SE">Sweden</country></aff><aff id="aff3"><label>3</label><institution>Heidelberg Institute of Public Health</institution>, Im Neuenheimer Feld 130/3, 69120 Heidelberg,<country country="DE">Germany</country></aff><aff id="aff4"><label>4</label><institution>Department of Epidemiology and Global Health</institution>, Umeå University, SE-90187 Umeå,<country country="SE">Sweden</country></aff><author-notes><corresp id="cor1">To whom correspondence should be addressed. Tel. <phone>+46706361635</phone>; Email: <email>joacim.rocklov@umu.se</email></corresp></author-notes><pub-date pub-type="collection"><month>3</month><year>2020</year></pub-date><pub-date pub-type="epub" iso-8601-date="2020-02-13"><day>13</day><month>2</month><year>2020</year></pub-date><pub-date pub-type="pmc-release"><day>13</day><month>2</month><year>2020</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>27</volume><issue>2</issue><elocation-id>taaa021</elocation-id><permissions><copyright-statement>© International Society of Travel Medicine 2020.</copyright-statement><copyright-year>2020</copyright-year><license license-type="cc-by-nc-sa" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/">http://creativecommons.org/licenses/by-nc/4.0/</ext-link>), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com</license-p></license></permissions><self-uri xlink:href="taaa021.pdf"></self-uri><kwd-group><kwd>Coronavirus</kwd><kwd>Wuhan</kwd><kwd>China</kwd><kwd>SARS</kwd><kwd>Public health emergency of international concern</kwd><kwd>COVID-19</kwd><kwd>Epidemic potential</kwd><kwd><italic>R</italic><sub>0</sub></kwd></kwd-group><counts><page-count count="4"></page-count></counts></article-meta></front><body><sec id="sec1"><title>Introduction</title><p>In Wuhan, China, a novel and alarmingly contagious primary atypical (viral) pneumonia broke out in December 2019. It has since been identified as a zoonotic coronavirus, similar to SARS coronavirus and MERS coronavirus and named COVID-19. As of 8 February 2020, 33 738 confirmed cases and 811 deaths have been reported in China.</p><p>Here we review the basic reproduction number (<italic>R</italic><sub>0</sub>) of the COVID-19 virus. <italic>R</italic><sub>0</sub> is an indication of the transmissibility of a virus, representing the average number of new infections generated by an infectious person in a totally naïve population. For <italic>R</italic><sub>0</sub> > 1, the number infected is likely to increase, and for <italic>R</italic><sub>0</sub> < 1, transmission is likely to die out. The basic reproduction number is a central concept in infectious disease epidemiology, indicating the risk of an infectious agent with respect to epidemic spread.</p></sec><sec id="sec2"><title>Methods and Results</title><p>PubMed, bioRxiv and Google Scholar were accessed to search for eligible studies. The term ‘coronavirus & basic reproduction number’ was used. The time period covered was from 1 January 2020 to 7 February 2020. For this time period, we identified 12 studies which estimated the basic reproductive number for COVID-19 from China and overseas. <xref rid="TB1" ref-type="table">Table 1</xref> shows that the estimates ranged from 1.4 to 6.49, with a mean of 3.28, a median of 2.79 and interquartile range (IQR) of 1.16.</p><table-wrap id="TB1" orientation="portrait" position="float"><label>Table 1</label><caption><p>Published estimates of <italic>R</italic><sub>0</sub> for 2019-nCoV</p></caption><table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col><col align="left" span="1"></col><col align="left" span="1"></col><col align="left" span="1"></col><col align="left" span="1"></col><col align="left" span="1"></col><col align="left" span="1"></col></colgroup><thead><tr><td rowspan="1" colspan="1">Study (study year)</td><td rowspan="1" colspan="1">Location</td><td rowspan="1" colspan="1">Study date</td><td rowspan="1" colspan="1">Methods</td><td rowspan="1" colspan="1">Approaches</td><td rowspan="1" colspan="1"><italic>R</italic><sub>0</sub> estimates (average)</td><td rowspan="1" colspan="1">95% CI</td></tr></thead><tbody><tr><td rowspan="1" colspan="1">Joseph <italic>et al</italic>.<xref rid="ref1" ref-type="bibr"><sup>1</sup></xref></td><td rowspan="1" colspan="1">Wuhan</td><td rowspan="1" colspan="1">31 December 2019–28 January 2020</td><td rowspan="1" colspan="1">Stochastic Markov Chain Monte Carlo methods (MCMC)</td><td rowspan="1" colspan="1">MCMC methods with Gibbs sampling and non-informative flat prior, using posterior distribution</td><td rowspan="1" colspan="1">2.68</td><td rowspan="1" colspan="1">2.47–2.86</td></tr><tr><td rowspan="1" colspan="1">Shen <italic>et al</italic>.<xref rid="ref2" ref-type="bibr"><sup>2</sup></xref></td><td rowspan="1" colspan="1">Hubei province</td><td rowspan="1" colspan="1">12–22 January 2020</td><td rowspan="1" colspan="1">Mathematical model, dynamic compartmental model with population divided into five compartments: susceptible individuals, asymptomatic individuals during the incubation period, infectious individuals with symptoms, isolated individuals with treatment and recovered individuals</td><td rowspan="1" colspan="1"><italic>R</italic><sub>0</sub> = <inline-formula><tex-math id="M1">\documentclass[12pt]{minimal}
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}{}$\beta$\end{document}</tex-math></inline-formula> = mean person-to-person transmission rate/day in the absence of control interventions, using nonlinear least squares method to get its point estimate<inline-formula><tex-math id="M4">\documentclass[12pt]{minimal}
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}{}$\alpha$\end{document}</tex-math></inline-formula> = isolation rate = 6</td><td rowspan="1" colspan="1">6.49</td><td rowspan="1" colspan="1">6.31–6.66</td></tr><tr><td rowspan="1" colspan="1">Liu <italic>et al</italic>.<xref rid="ref3" ref-type="bibr"><sup>3</sup></xref></td><td rowspan="1" colspan="1">China and overseas</td><td rowspan="1" colspan="1">23 January 2020</td><td rowspan="1" colspan="1">Statistical exponential Growth, using SARS generation time = 8.4 days, SD = 3.8 days</td><td rowspan="1" colspan="1">Applies Poisson regression to fit the exponential growth rate<italic>R</italic><sub>0</sub> = 1/<italic>M</italic>(−𝑟)<italic>M</italic> = moment generating function of the generation time distribution<italic>r</italic> = fitted exponential growth rate</td><td rowspan="1" colspan="1">2.90</td><td rowspan="1" colspan="1">2.32–3.63</td></tr><tr><td rowspan="1" colspan="1">Liu <italic>et al</italic>.<xref rid="ref3" ref-type="bibr"><sup>3</sup></xref></td><td rowspan="1" colspan="1">China and overseas</td><td rowspan="1" colspan="1">23 January 2020</td><td rowspan="1" colspan="1">Statistical maximum likelihood estimation, using SARS generation time = 8.4 days, SD = 3.8 days</td><td rowspan="1" colspan="1">Maximize log-likelihood to estimate <italic>R</italic><sub>0</sub> by using surveillance data during a disease epidemic, and assuming the secondary case is Poisson distribution with expected value <italic>R</italic><sub>0</sub></td><td rowspan="1" colspan="1">2.92</td><td rowspan="1" colspan="1">2.28–3.67</td></tr><tr><td rowspan="1" colspan="1">Read <italic>et al</italic>.<xref rid="ref4" ref-type="bibr"><sup>4</sup></xref></td><td rowspan="1" colspan="1">China</td><td rowspan="1" colspan="1">1–22 January 2020</td><td rowspan="1" colspan="1">Mathematical transmission model assuming latent period = 4 days and near to the incubation period</td><td rowspan="1" colspan="1">Assumes daily time increments with Poisson-distribution and apply a deterministic SEIR metapopulation transmission model, transmission rate = 1.94, infectious period =1.61 days</td><td rowspan="1" colspan="1">3.11</td><td rowspan="1" colspan="1">2.39–4.13</td></tr><tr><td rowspan="1" colspan="1">Majumder <italic>et al</italic>.<xref rid="ref5" ref-type="bibr"><sup>5</sup></xref></td><td rowspan="1" colspan="1">Wuhan</td><td rowspan="1" colspan="1">8 December 2019 and 26 January 2020</td><td rowspan="1" colspan="1">Mathematical Incidence Decay and Exponential Adjustment (IDEA) model</td><td rowspan="1" colspan="1">Adopted mean serial interval lengths from SARS and MERS ranging from 6 to 10 days to fit the IDEA model,</td><td rowspan="1" colspan="1">2.0–3.1 (2.55)</td><td rowspan="1" colspan="1">/</td></tr><tr><td rowspan="1" colspan="1">WHO</td><td rowspan="1" colspan="1">China</td><td rowspan="1" colspan="1">18 January 2020</td><td rowspan="1" colspan="1">/</td><td rowspan="1" colspan="1">/</td><td rowspan="1" colspan="1">1.4–2.5 (1.95)</td><td rowspan="1" colspan="1">/</td></tr><tr><td rowspan="1" colspan="1">Cao <italic>et al</italic>.<xref rid="ref6" ref-type="bibr"><sup>6</sup></xref></td><td rowspan="1" colspan="1">China</td><td rowspan="1" colspan="1">23 January 2020</td><td rowspan="1" colspan="1">Mathematical model including compartments Susceptible-Exposed-Infectious-Recovered-Death-Cumulative (SEIRDC)</td><td rowspan="1" colspan="1">R = <italic>K</italic> 2 (<italic>L</italic> × <italic>D</italic>) + <italic>K</italic>(<italic>L</italic> + <italic>D</italic>) + 1<italic>L</italic> = average latent period = 7,<italic>D</italic> = average latent infectious period = 9,<italic>K</italic> = logarithmic growth rate of the case counts</td><td rowspan="1" colspan="1">4.08</td><td rowspan="1" colspan="1">/</td></tr><tr><td rowspan="1" colspan="1">Zhao <italic>et al</italic>.<xref rid="ref7" ref-type="bibr"><sup>7</sup></xref></td><td rowspan="1" colspan="1">China</td><td rowspan="1" colspan="1">10–24 January 2020</td><td rowspan="1" colspan="1">Statistical exponential growth model method adopting serial interval from SARS (mean = 8.4 days, SD = 3.8 days) and MERS (mean = 7.6 days, SD = 3.4 days)</td><td rowspan="1" colspan="1">Corresponding to 8-fold increase in the reporting rate<italic>R</italic><sub>0</sub> = 1/<italic>M</italic>(−𝑟)𝑟 =intrinsic growth rate<italic>M</italic> = moment generating function</td><td rowspan="1" colspan="1">2.24</td><td rowspan="1" colspan="1">1.96–2.55</td></tr><tr><td rowspan="1" colspan="1">Zhao <italic>et al</italic>.<xref rid="ref7" ref-type="bibr"><sup>7</sup></xref></td><td rowspan="1" colspan="1">China</td><td rowspan="1" colspan="1">10–24 January 2020</td><td rowspan="1" colspan="1">Statistical exponential growth model method adopting serial interval from SARS (mean = 8.4 days, SD = 3.8 days) and MERS (mean = 7.6 days, SD = 3.4 days)</td><td rowspan="1" colspan="1">Corresponding to 2-fold increase in the reporting rate<italic>R</italic><sub>0</sub> = 1/<italic>M</italic>(−𝑟)𝑟 =intrinsic growth rate<italic>M</italic> = moment generating function</td><td rowspan="1" colspan="1">3.58</td><td rowspan="1" colspan="1">2.89–4.39</td></tr><tr><td rowspan="1" colspan="1">Imai (2020)<xref rid="ref8" ref-type="bibr"><sup>8</sup></xref></td><td rowspan="1" colspan="1">Wuhan</td><td rowspan="1" colspan="1">January 18, 2020</td><td rowspan="1" colspan="1">Mathematical model, computational modelling of potential epidemic trajectories</td><td rowspan="1" colspan="1">Assume SARS-like levels of case-to-case variability in the numbers of secondary cases and a SARS-like generation time with 8.4 days, and set number of cases caused by zoonotic exposure and assumed total number of cases to estimate <italic>R</italic><sub>0</sub> values for best-case, median and worst-case</td><td rowspan="1" colspan="1">1.5–3.5 (2.5)</td><td rowspan="1" colspan="1">/</td></tr><tr><td rowspan="1" colspan="1">Julien and Althaus<xref rid="ref9" ref-type="bibr"><sup>9</sup></xref></td><td rowspan="1" colspan="1">China and overseas</td><td rowspan="1" colspan="1">18 January 2020</td><td rowspan="1" colspan="1">Stochastic simulations of early outbreak trajectories</td><td rowspan="1" colspan="1">Stochastic simulations of early outbreak trajectories were performed that are consistent with the epidemiological findings to date</td><td rowspan="1" colspan="1">2.2</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1">Tang <italic>et al</italic>.<xref rid="ref10" ref-type="bibr"><sup>10</sup></xref></td><td rowspan="1" colspan="1">China</td><td rowspan="1" colspan="1">22 January 2020</td><td rowspan="1" colspan="1">Mathematical SEIR-type epidemiological model incorporates appropriate compartments corresponding to interventions</td><td rowspan="1" colspan="1">Method-based method and Likelihood-based method</td><td rowspan="1" colspan="1">6.47</td><td rowspan="1" colspan="1">5.71–7.23</td></tr><tr><td rowspan="1" colspan="1">Qun Li <italic>et al</italic>.<xref rid="ref11" ref-type="bibr"><sup>11</sup></xref></td><td rowspan="1" colspan="1">China</td><td rowspan="1" colspan="1">22 January 2020</td><td rowspan="1" colspan="1">Statistical exponential growth model</td><td rowspan="1" colspan="1">Mean incubation period = 5.2 days, mean serial interval = 7.5 days</td><td rowspan="1" colspan="1">2.2</td><td rowspan="1" colspan="1">1.4–3.9</td></tr><tr><td colspan="5" rowspan="1">Averaged</td><td rowspan="1" colspan="1">3.28</td></tr></tbody></table><table-wrap-foot><p>CI, Confidence interval.</p></table-wrap-foot></table-wrap><fig id="f1" orientation="portrait" position="float"><label>Figure 1</label><caption><p>Timeline of the <italic>R</italic><sub>0</sub> estimates for the 2019-nCoV virus in China</p></caption><graphic xlink:href="taaa021f1"></graphic></fig><p>The first studies initially reported estimates of <italic>R</italic><sub>0</sub> with lower values. Estimations subsequently increased and then again returned in the most recent estimates to the levels initially reported (Figure 1). A closer look reveals that the estimation method used played a role.</p><p>The two studies using stochastic methods to estimate <italic>R</italic><sub>0</sub>, reported a range of 2.2–2.68 with an average of 2.44.<xref rid="ref1" ref-type="bibr"><sup>1</sup></xref><sup>,</sup><xref rid="ref9" ref-type="bibr"><sup>9</sup></xref> The six studies using mathematical methods to estimate <italic>R</italic><sub>0</sub> produced a range from 1.5 to 6.49, with an average of 4.2.<xref rid="ref2" ref-type="bibr"><sup>2</sup></xref><sup>,</sup><xref rid="ref4" ref-type="bibr"><sup>4–6</sup></xref><sup>,</sup><xref rid="ref8" ref-type="bibr"><sup>8</sup></xref><sup>,</sup><xref rid="ref10" ref-type="bibr"><sup>10</sup></xref> The three studies using statistical methods such as exponential growth estimated an <italic>R</italic><sub>0</sub> ranging from 2.2 to 3.58, with an average of 2.67.<xref rid="ref3" ref-type="bibr"><sup>3</sup></xref><sup>,</sup><xref rid="ref7" ref-type="bibr"><sup>7</sup></xref><sup>,</sup><xref rid="ref11" ref-type="bibr"><sup>11</sup></xref></p></sec><sec id="sec3"><title>Discussion</title><p>Our review found the average <italic>R</italic><sub>0</sub> to be 3.28 and median to be 2.79, which exceed WHO estimates from 1.4 to 2.5. The studies using stochastic and statistical methods for deriving <italic>R</italic><sub>0</sub> provide estimates that are reasonably comparable. However, the studies using mathematical methods produce estimates that are, on average, higher. Some of the mathematically derived estimates fall within the range produced the statistical and stochastic estimates. It is important to further assess the reason for the higher <italic>R</italic><sub>0</sub> values estimated by some the mathematical studies. For example, modelling assumptions may have played a role. In more recent studies, <italic>R</italic><sub>0</sub> seems to have stabilized at around 2–3. <italic>R</italic><sub>0</sub> estimations produced at later stages can be expected to be more reliable, as they build upon more case data and include the effect of awareness and intervention. It is worthy to note that the WHO point estimates are consistently below all published estimates, although the higher end of the WHO range includes the lower end of the estimates reviewed here.</p><p><italic>R</italic><sub>0</sub> estimates for SARS have been reported to range between 2 and 5, which is within the range of the mean <italic>R</italic><sub>0</sub> for COVID-19 found in this review. Due to similarities of both pathogen and region of exposure, this is expected. On the other hand, despite the heightened public awareness and impressively strong interventional response, the COVID-19 is already more widespread than SARS, indicating it may be more transmissible.</p></sec><sec id="sec4"><title>Conclusions</title><p>This review found that the estimated mean <italic>R</italic><sub>0</sub> for COVID-19 is around 3.28, with a median of 2.79 and IQR of 1.16, which is considerably higher than the WHO estimate at 1.95. These estimates of <italic>R</italic><sub>0</sub> depend on the estimation method used as well as the validity of the underlying assumptions. Due to insufficient data and short onset time, current estimates of <italic>R</italic><sub>0</sub> for COVID-19 are possibly biased. However, as more data are accumulated, estimation error can be expected to decrease and a clearer picture should form. Based on these considerations, <italic>R</italic><sub>0</sub> for COVID-19 is expected to be around 2–3, which is broadly consistent with the WHO estimate.</p></sec><sec id="sec5"><title>Author contributions</title><p>J.R. and A.W.S. had the idea, and Y.L. did the literature search and created the table and figure. Y.L. and A.W.S. wrote the first draft; A.A.G. drafted the final manuscript. All authors contributed to the final manuscript.</p></sec><sec id="sec6"><title>Conflict of interest</title><p>None declared.</p></sec></body><back><notes><p><bold>Teaser:</bold> Our review found the average <italic>R</italic><sub>0</sub> for COVID-19 to be 3.28, which exceeds WHO estimates from 1.4 to 2.5.</p></notes><ref-list id="bib1"><title>References</title><ref id="ref1"><label>1.</label><mixed-citation publication-type="other"><person-group person-group-type="author"><name name-style="western"><surname>Wu</surname><given-names>JT</given-names></name>, <name name-style="western"><surname>Leung</surname><given-names>K</given-names></name>, <name name-style="western"><surname>Leung</surname><given-names>GM</given-names></name></person-group><article-title>Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study</article-title>, <comment>The Lancet, 2020</comment>.</mixed-citation></ref><ref 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