reproductive number of COVID-19 is higher compared to SARS coronavirus | Journal of Travel Medicine

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class="al-author-name-more js-flyout-wrap"> <button type="button" class="linked-name js-linked-name-trigger btn-as-link">Ying Liu</button><span class='delimiter'>, </span> <span class="al-author-info-wrap arrow-up"> <div class="info-card-author authorInfo_OUP_ArticleTop_Info_Widget"> <div class="name-role-wrap"> <div class="info-card-name"> Ying Liu </div> </div> <div class="info-card-affilitation"> <div class="aff"><div class="institution">School of International Business</div>, Xiamen University Tan Kah Kee College, Zhangzhou, 363105, <div class="country">China</div></div> </div> <div class="info-card-search-label"> Search for other works by this author on: </div> <div class="info-card-search info-card-search-internal"> <a href="/jtm/search-results?f_Authors=Ying+Liu" rel="nofollow">Oxford Academic</a> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Liu Y">PubMed</a> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Liu Ying%22">Google Scholar</a> </div> </div> </span> </span> <span class="al-author-name-more js-flyout-wrap"> <button type="button" class="linked-name js-linked-name-trigger btn-as-link">Albert A Gayle</button><span class='delimiter'>, </span> <span class="al-author-info-wrap arrow-up"> <div class="info-card-author authorInfo_OUP_ArticleTop_Info_Widget"> <div class="name-role-wrap"> <div class="info-card-name"> Albert A Gayle </div> </div> <div class="info-card-affilitation"> <div class="aff"><div class="institution">Department of Public Health and Clinical Medicine</div>, Section of Sustainable Health, Umeå University, SE-90187 Umeå, <div class="country">Sweden</div></div> </div> <div class="info-card-search-label"> Search for other works by this author on: </div> <div class="info-card-search info-card-search-internal"> <a href="/jtm/search-results?f_Authors=Albert+A+Gayle" rel="nofollow">Oxford Academic</a> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Gayle A">PubMed</a> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Gayle Albert A%22">Google Scholar</a> </div> </div> </span> </span> <span class="al-author-name-more js-flyout-wrap"> <button type="button" class="linked-name js-linked-name-trigger btn-as-link">Annelies Wilder-Smith</button><span class='delimiter'>, </span> <span class="al-author-info-wrap arrow-up"> <div class="info-card-author authorInfo_OUP_ArticleTop_Info_Widget"> <div class="name-role-wrap"> <div class="info-card-name"> Annelies Wilder-Smith </div> </div> <div class="info-card-affilitation"> <div class="aff"><div class="institution">Heidelberg Institute of Public Health</div>, Im Neuenheimer Feld 130/3, 69120 Heidelberg, <div class="country">Germany</div></div><div class="aff"><div class="institution">Department of Epidemiology and Global Health</div>, Umeå University, SE-90187 Umeå, <div class="country">Sweden</div></div> </div> <div class="info-card-search-label"> Search for other works by this author on: </div> <div class="info-card-search info-card-search-internal"> <a href="/jtm/search-results?f_Authors=Annelies+Wilder-Smith" rel="nofollow">Oxford Academic</a> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Wilder-Smith A">PubMed</a> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Wilder-Smith Annelies%22">Google Scholar</a> </div> </div> </span> </span> <span class="al-author-name-more js-flyout-wrap"> <button type="button" class="linked-name js-linked-name-trigger btn-as-link">Joacim Rocklöv</button><span class='delimiter'></span> <span class="al-author-info-wrap arrow-up"> <div class="info-card-author authorInfo_OUP_ArticleTop_Info_Widget"> <div class="name-role-wrap"> <div class="info-card-name"> Joacim Rocklöv <span class="info-card-footnote"><span class="xrefLink" id="jumplink-cor1"></span><a href="javascript:;" reveal-id="cor1" data-open="cor1" class="link link-ref link-reveal xref-default"></a></span> </div> </div> <div class="info-card-affilitation"> <div class="aff"><div class="institution">Department of Public Health and Clinical Medicine</div>, Section of Sustainable Health, Umeå University, SE-90187 Umeå, <div class="country">Sweden</div></div> </div> <div class="info-author-correspondence"> <div content-id="cor1">To whom correspondence should be addressed. Tel. +46706361635; Email: <a href="mailto:joacim.rocklov@umu.se" target="_blank">joacim.rocklov@umu.se</a></div> </div> <div class="info-card-search-label"> Search for other works by this author on: </div> <div class="info-card-search info-card-search-internal"> <a href="/jtm/search-results?f_Authors=Joacim+Rockl%c3%b6v" rel="nofollow">Oxford Academic</a> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Rocklöv J">PubMed</a> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Rocklöv Joacim%22">Google Scholar</a> </div> </div> </span> </span> </div> </div> <div class="pub-history-wrap clearfix js-history-dropdown-wrap"> <div class="pub-history-row clearfix"> <div class="ww-citation-primary"><em>Journal of Travel Medicine</em>, Volume 27, Issue 2, March 2020, taaa021, <a 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widget-ArticleFulltext widget-instance-OUP_Article_FullText_Widget"> <div class="module-widget"> <div class="widget-items" data-widgetname="ArticleFulltext"> <div class="kwd-group"><a class="kwd-part kwd-main" href="javascript:;" data-keyword="Coronavirus">Coronavirus</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword="Wuhan">Wuhan</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword="China">China</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword="SARS">SARS</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword=""Public health emergency of international concern"">Public health emergency of international concern</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword="COVID-19">COVID-19</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword=""Epidemic potential"">Epidemic potential</a>, <a class="kwd-part kwd-main" href="javascript:;" data-keyword="R0"><em>R</em><sub>0</sub></a></div> <h2 scrollto-destination=200748290 id="200748290" class="section-title js-splitscreen-section-title" data-legacy-id=sec1>Introduction</h2> <p class="chapter-para">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 class="chapter-para">Here we review the basic reproduction number (<em>R</em><sub>0</sub>) of the COVID-19 virus. <em>R</em><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 <em>R</em><sub>0</sub> > 1, the number infected is likely to increase, and for <em>R</em><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> <h2 scrollto-destination=200748293 id="200748293" class="section-title js-splitscreen-section-title" data-legacy-id=sec2>Methods and Results</h2> <p class="chapter-para">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. <span class="xrefLink" id="jumplink-TB1"></span><a href="javascript:;" reveal-id="TB1" data-open="TB1" class="link link-reveal link-table xref-fig">Table 1</a> 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> <a id="200748295" scrollto-destination="200748295"></a> <div content-id="TB1" class="table-modal table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB1" data-id="TB1"><span class="label title-label" id="label-12706">Table 1</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/200748295" aria-describedby="label-12706"> Open in new tab </a></div><div class="caption caption-id-" id="caption-12706"><p class="chapter-para">Published estimates of <em>R</em><sub>0</sub> for 2019-nCoV</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-12706" aria-describedby="
 caption-12706"><thead><tr><td>Study (study year)</td><td>Location</td><td>Study date</td><td>Methods</td><td>Approaches</td><td><em>R</em><sub>0</sub> estimates (average)</td><td>95% CI</td></tr></thead><tbody><tr><td>Joseph <em>et al</em>.<span class="xrefLink" id="jumplink-ref1"></span><a href="javascript:;" reveal-id="ref1" data-open="ref1" class="link link-ref link-reveal xref-bibr"><sup>1</sup></a></td><td>Wuhan</td><td>31 December 2019–28 January 2020</td><td>Stochastic Markov Chain Monte Carlo methods (MCMC)</td><td>MCMC methods with Gibbs sampling and non-informative flat prior, using posterior distribution</td><td>2.68</td><td>2.47–2.86</td></tr><tr><td>Shen <em>et al</em>.<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr"><sup>2</sup></a></td><td>Hubei province</td><td>12–22 January 2020</td><td>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><em>R</em><sub>0</sub> = <span class="inline-formula no-formula-id">|$\beta$|</span>/<span class="inline-formula no-formula-id">|$\alpha$|</span><span class="inline-formula no-formula-id">|$\beta$|</span> = mean person-to-person transmission rate/day in the absence of control interventions, using nonlinear least squares method to get its point estimate<span class="inline-formula no-formula-id">|$\alpha$|</span> = isolation rate = 6</td><td>6.49</td><td>6.31–6.66</td></tr><tr><td>Liu <em>et al</em>.<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr"><sup>3</sup></a></td><td>China and overseas</td><td>23 January 2020</td><td>Statistical exponential Growth, using SARS generation time = 8.4 days, SD = 3.8 days</td><td>Applies Poisson regression to fit the exponential growth rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)<em>M</em> = moment generating function of the generation time distribution<em>r</em> = fitted exponential growth rate</td><td>2.90</td><td>2.32–3.63</td></tr><tr><td>Liu <em>et al</em>.<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr"><sup>3</sup></a></td><td>China and overseas</td><td>23 January 2020</td><td>Statistical maximum likelihood estimation, using SARS generation time = 8.4 days, SD = 3.8 days</td><td>Maximize log-likelihood to estimate <em>R</em><sub>0</sub> by using surveillance data during a disease epidemic, and assuming the secondary case is Poisson distribution with expected value <em>R</em><sub>0</sub></td><td>2.92</td><td>2.28–3.67</td></tr><tr><td>Read <em>et al</em>.<span class="xrefLink" id="jumplink-ref4"></span><a href="javascript:;" reveal-id="ref4" data-open="ref4" class="link link-ref link-reveal xref-bibr"><sup>4</sup></a></td><td>China</td><td>1–22 January 2020</td><td>Mathematical transmission model assuming latent period = 4 days and near to the incubation period</td><td>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>3.11</td><td>2.39–4.13</td></tr><tr><td>Majumder <em>et al</em>.<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr"><sup>5</sup></a></td><td>Wuhan</td><td>8 December 2019 and 26 January 2020</td><td>Mathematical Incidence Decay and Exponential Adjustment (IDEA) model</td><td>Adopted mean serial interval lengths from SARS and MERS ranging from 6 to 10 days to fit the IDEA model,</td><td>2.0–3.1 (2.55)</td><td>/</td></tr><tr><td>WHO</td><td>China</td><td>18 January 2020</td><td>/</td><td>/</td><td>1.4–2.5 (1.95)</td><td>/</td></tr><tr><td>Cao <em>et al</em>.<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr"><sup>6</sup></a></td><td>China</td><td>23 January 2020</td><td>Mathematical model including compartments Susceptible-Exposed-Infectious-Recovered-Death-Cumulative (SEIRDC)</td><td>R = <em>K</em> 2 (<em>L</em> × <em>D</em>) + <em>K</em>(<em>L</em> + <em>D</em>) + 1<em>L</em> = average latent period = 7,<em>D</em> = average latent infectious period = 9,<em>K</em> = logarithmic growth rate of the case counts</td><td>4.08</td><td>/</td></tr><tr><td>Zhao <em>et al</em>.<span class="xrefLink" id="jumplink-ref7"></span><a href="javascript:;" reveal-id="ref7" data-open="ref7" class="link link-ref link-reveal xref-bibr"><sup>7</sup></a></td><td>China</td><td>10–24 January 2020</td><td>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>Corresponding to 8-fold increase in the reporting rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)𝑟 =intrinsic growth rate<em>M</em> = moment generating function</td><td>2.24</td><td>1.96–2.55</td></tr><tr><td>Zhao <em>et al</em>.<span class="xrefLink" id="jumplink-ref7"></span><a href="javascript:;" reveal-id="ref7" data-open="ref7" class="link link-ref link-reveal xref-bibr"><sup>7</sup></a></td><td>China</td><td>10–24 January 2020</td><td>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>Corresponding to 2-fold increase in the reporting rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)𝑟 =intrinsic growth rate<em>M</em> = moment generating function</td><td>3.58</td><td>2.89–4.39</td></tr><tr><td>Imai (2020)<span class="xrefLink" id="jumplink-ref8"></span><a href="javascript:;" reveal-id="ref8" data-open="ref8" class="link link-ref link-reveal xref-bibr"><sup>8</sup></a></td><td>Wuhan</td><td>January 18, 2020</td><td>Mathematical model, computational modelling of potential epidemic trajectories</td><td>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 <em>R</em><sub>0</sub> values for best-case, median and worst-case</td><td>1.5–3.5 (2.5)</td><td>/</td></tr><tr><td>Julien and Althaus<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr"><sup>9</sup></a></td><td>China and overseas</td><td>18 January 2020</td><td>Stochastic simulations of early outbreak trajectories</td><td>Stochastic simulations of early outbreak trajectories were performed that are consistent with the epidemiological findings to date</td><td>2.2</td><td></td></tr><tr><td>Tang <em>et al</em>.<span class="xrefLink" id="jumplink-ref10"></span><a href="javascript:;" reveal-id="ref10" data-open="ref10" class="link link-ref link-reveal xref-bibr"><sup>10</sup></a></td><td>China</td><td>22 January 2020</td><td>Mathematical SEIR-type epidemiological model incorporates appropriate compartments corresponding to interventions</td><td>Method-based method and Likelihood-based method</td><td>6.47</td><td>5.71–7.23</td></tr><tr><td>Qun Li <em>et al</em>.<span class="xrefLink" id="jumplink-ref11"></span><a href="javascript:;" reveal-id="ref11" data-open="ref11" class="link link-ref link-reveal xref-bibr"><sup>11</sup></a></td><td>China</td><td>22 January 2020</td><td>Statistical exponential growth model</td><td>Mean incubation period = 5.2 days, mean serial interval = 7.5 days</td><td>2.2</td><td>1.4–3.9</td></tr><tr><td colspan="5">Averaged</td><td>3.28</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><td>Study (study year)</td><td>Location</td><td>Study date</td><td>Methods</td><td>Approaches</td><td><em>R</em><sub>0</sub> estimates (average)</td><td>95% CI</td></tr></thead><tbody><tr><td>Joseph <em>et al</em>.<span class="xrefLink" id="jumplink-ref1"></span><a href="javascript:;" reveal-id="ref1" data-open="ref1" class="link link-ref link-reveal xref-bibr"><sup>1</sup></a></td><td>Wuhan</td><td>31 December 2019–28 January 2020</td><td>Stochastic Markov Chain Monte Carlo methods (MCMC)</td><td>MCMC methods with Gibbs sampling and non-informative flat prior, using posterior distribution</td><td>2.68</td><td>2.47–2.86</td></tr><tr><td>Shen <em>et al</em>.<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr"><sup>2</sup></a></td><td>Hubei province</td><td>12–22 January 2020</td><td>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><em>R</em><sub>0</sub> = <span class="inline-formula no-formula-id">|$\beta$|</span>/<span class="inline-formula no-formula-id">|$\alpha$|</span><span class="inline-formula no-formula-id">|$\beta$|</span> = mean person-to-person transmission rate/day in the absence of control interventions, using nonlinear least squares method to get its point estimate<span class="inline-formula no-formula-id">|$\alpha$|</span> = isolation rate = 6</td><td>6.49</td><td>6.31–6.66</td></tr><tr><td>Liu <em>et al</em>.<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr"><sup>3</sup></a></td><td>China and overseas</td><td>23 January 2020</td><td>Statistical exponential Growth, using SARS generation time = 8.4 days, SD = 3.8 days</td><td>Applies Poisson regression to fit the exponential growth rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)<em>M</em> = moment generating function of the generation time distribution<em>r</em> = fitted exponential growth rate</td><td>2.90</td><td>2.32–3.63</td></tr><tr><td>Liu <em>et al</em>.<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr"><sup>3</sup></a></td><td>China and overseas</td><td>23 January 2020</td><td>Statistical maximum likelihood estimation, using SARS generation time = 8.4 days, SD = 3.8 days</td><td>Maximize log-likelihood to estimate <em>R</em><sub>0</sub> by using surveillance data during a disease epidemic, and assuming the secondary case is Poisson distribution with expected value <em>R</em><sub>0</sub></td><td>2.92</td><td>2.28–3.67</td></tr><tr><td>Read <em>et al</em>.<span class="xrefLink" id="jumplink-ref4"></span><a href="javascript:;" reveal-id="ref4" data-open="ref4" class="link link-ref link-reveal xref-bibr"><sup>4</sup></a></td><td>China</td><td>1–22 January 2020</td><td>Mathematical transmission model assuming latent period = 4 days and near to the incubation period</td><td>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>3.11</td><td>2.39–4.13</td></tr><tr><td>Majumder <em>et al</em>.<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr"><sup>5</sup></a></td><td>Wuhan</td><td>8 December 2019 and 26 January 2020</td><td>Mathematical Incidence Decay and Exponential Adjustment (IDEA) model</td><td>Adopted mean serial interval lengths from SARS and MERS ranging from 6 to 10 days to fit the IDEA model,</td><td>2.0–3.1 (2.55)</td><td>/</td></tr><tr><td>WHO</td><td>China</td><td>18 January 2020</td><td>/</td><td>/</td><td>1.4–2.5 (1.95)</td><td>/</td></tr><tr><td>Cao <em>et al</em>.<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr"><sup>6</sup></a></td><td>China</td><td>23 January 2020</td><td>Mathematical model including compartments Susceptible-Exposed-Infectious-Recovered-Death-Cumulative (SEIRDC)</td><td>R = <em>K</em> 2 (<em>L</em> × <em>D</em>) + <em>K</em>(<em>L</em> + <em>D</em>) + 1<em>L</em> = average latent period = 7,<em>D</em> = average latent infectious period = 9,<em>K</em> = logarithmic growth rate of the case counts</td><td>4.08</td><td>/</td></tr><tr><td>Zhao <em>et al</em>.<span class="xrefLink" id="jumplink-ref7"></span><a href="javascript:;" reveal-id="ref7" data-open="ref7" class="link link-ref link-reveal xref-bibr"><sup>7</sup></a></td><td>China</td><td>10–24 January 2020</td><td>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>Corresponding to 8-fold increase in the reporting rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)𝑟 =intrinsic growth rate<em>M</em> = moment generating function</td><td>2.24</td><td>1.96–2.55</td></tr><tr><td>Zhao <em>et al</em>.<span class="xrefLink" id="jumplink-ref7"></span><a href="javascript:;" reveal-id="ref7" data-open="ref7" class="link link-ref link-reveal xref-bibr"><sup>7</sup></a></td><td>China</td><td>10–24 January 2020</td><td>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>Corresponding to 2-fold increase in the reporting rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)𝑟 =intrinsic growth rate<em>M</em> = moment generating function</td><td>3.58</td><td>2.89–4.39</td></tr><tr><td>Imai (2020)<span class="xrefLink" id="jumplink-ref8"></span><a href="javascript:;" reveal-id="ref8" data-open="ref8" class="link link-ref link-reveal xref-bibr"><sup>8</sup></a></td><td>Wuhan</td><td>January 18, 2020</td><td>Mathematical model, computational modelling of potential epidemic trajectories</td><td>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 <em>R</em><sub>0</sub> values for best-case, median and worst-case</td><td>1.5–3.5 (2.5)</td><td>/</td></tr><tr><td>Julien and Althaus<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr"><sup>9</sup></a></td><td>China and overseas</td><td>18 January 2020</td><td>Stochastic simulations of early outbreak trajectories</td><td>Stochastic simulations of early outbreak trajectories were performed that are consistent with the epidemiological findings to date</td><td>2.2</td><td></td></tr><tr><td>Tang <em>et al</em>.<span class="xrefLink" id="jumplink-ref10"></span><a href="javascript:;" reveal-id="ref10" data-open="ref10" class="link link-ref link-reveal xref-bibr"><sup>10</sup></a></td><td>China</td><td>22 January 2020</td><td>Mathematical SEIR-type epidemiological model incorporates appropriate compartments corresponding to interventions</td><td>Method-based method and Likelihood-based method</td><td>6.47</td><td>5.71–7.23</td></tr><tr><td>Qun Li <em>et al</em>.<span class="xrefLink" id="jumplink-ref11"></span><a href="javascript:;" reveal-id="ref11" data-open="ref11" class="link link-ref link-reveal xref-bibr"><sup>11</sup></a></td><td>China</td><td>22 January 2020</td><td>Statistical exponential growth model</td><td>Mean incubation period = 5.2 days, mean serial interval = 7.5 days</td><td>2.2</td><td>1.4–3.9</td></tr><tr><td colspan="5">Averaged</td><td>3.28</td></tr></tbody></table></div><div class="table-wrap-foot"><p class="chapter-para">CI, Confidence interval.</p></div></div></div><div class="table-full-width-wrap"><div class="table-wrap table-wide standard-table"><div class="table-wrap-title" id="TB1" data-id="TB1"><span class="label title-label" id="label-12706">Table 1</span><div class="
 graphic-wrap table-open-button-wrap
 "><a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" role="button" target="_blank" href="
 /view-large/200748295" aria-describedby="label-12706"> Open in new tab </a></div><div class="caption caption-id-" id="caption-12706"><p class="chapter-para">Published estimates of <em>R</em><sub>0</sub> for 2019-nCoV</p></div> </div><div class="table-overflow"><table role="table" aria-labelledby="
 label-12706" aria-describedby="
 caption-12706"><thead><tr><td>Study (study year)</td><td>Location</td><td>Study date</td><td>Methods</td><td>Approaches</td><td><em>R</em><sub>0</sub> estimates (average)</td><td>95% CI</td></tr></thead><tbody><tr><td>Joseph <em>et al</em>.<span class="xrefLink" id="jumplink-ref1"></span><a href="javascript:;" reveal-id="ref1" data-open="ref1" class="link link-ref link-reveal xref-bibr"><sup>1</sup></a></td><td>Wuhan</td><td>31 December 2019–28 January 2020</td><td>Stochastic Markov Chain Monte Carlo methods (MCMC)</td><td>MCMC methods with Gibbs sampling and non-informative flat prior, using posterior distribution</td><td>2.68</td><td>2.47–2.86</td></tr><tr><td>Shen <em>et al</em>.<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr"><sup>2</sup></a></td><td>Hubei province</td><td>12–22 January 2020</td><td>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><em>R</em><sub>0</sub> = <span class="inline-formula no-formula-id">|$\beta$|</span>/<span class="inline-formula no-formula-id">|$\alpha$|</span><span class="inline-formula no-formula-id">|$\beta$|</span> = mean person-to-person transmission rate/day in the absence of control interventions, using nonlinear least squares method to get its point estimate<span class="inline-formula no-formula-id">|$\alpha$|</span> = isolation rate = 6</td><td>6.49</td><td>6.31–6.66</td></tr><tr><td>Liu <em>et al</em>.<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr"><sup>3</sup></a></td><td>China and overseas</td><td>23 January 2020</td><td>Statistical exponential Growth, using SARS generation time = 8.4 days, SD = 3.8 days</td><td>Applies Poisson regression to fit the exponential growth rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)<em>M</em> = moment generating function of the generation time distribution<em>r</em> = fitted exponential growth rate</td><td>2.90</td><td>2.32–3.63</td></tr><tr><td>Liu <em>et al</em>.<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr"><sup>3</sup></a></td><td>China and overseas</td><td>23 January 2020</td><td>Statistical maximum likelihood estimation, using SARS generation time = 8.4 days, SD = 3.8 days</td><td>Maximize log-likelihood to estimate <em>R</em><sub>0</sub> by using surveillance data during a disease epidemic, and assuming the secondary case is Poisson distribution with expected value <em>R</em><sub>0</sub></td><td>2.92</td><td>2.28–3.67</td></tr><tr><td>Read <em>et al</em>.<span class="xrefLink" id="jumplink-ref4"></span><a href="javascript:;" reveal-id="ref4" data-open="ref4" class="link link-ref link-reveal xref-bibr"><sup>4</sup></a></td><td>China</td><td>1–22 January 2020</td><td>Mathematical transmission model assuming latent period = 4 days and near to the incubation period</td><td>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>3.11</td><td>2.39–4.13</td></tr><tr><td>Majumder <em>et al</em>.<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr"><sup>5</sup></a></td><td>Wuhan</td><td>8 December 2019 and 26 January 2020</td><td>Mathematical Incidence Decay and Exponential Adjustment (IDEA) model</td><td>Adopted mean serial interval lengths from SARS and MERS ranging from 6 to 10 days to fit the IDEA model,</td><td>2.0–3.1 (2.55)</td><td>/</td></tr><tr><td>WHO</td><td>China</td><td>18 January 2020</td><td>/</td><td>/</td><td>1.4–2.5 (1.95)</td><td>/</td></tr><tr><td>Cao <em>et al</em>.<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr"><sup>6</sup></a></td><td>China</td><td>23 January 2020</td><td>Mathematical model including compartments Susceptible-Exposed-Infectious-Recovered-Death-Cumulative (SEIRDC)</td><td>R = <em>K</em> 2 (<em>L</em> × <em>D</em>) + <em>K</em>(<em>L</em> + <em>D</em>) + 1<em>L</em> = average latent period = 7,<em>D</em> = average latent infectious period = 9,<em>K</em> = logarithmic growth rate of the case counts</td><td>4.08</td><td>/</td></tr><tr><td>Zhao <em>et al</em>.<span class="xrefLink" id="jumplink-ref7"></span><a href="javascript:;" reveal-id="ref7" data-open="ref7" class="link link-ref link-reveal xref-bibr"><sup>7</sup></a></td><td>China</td><td>10–24 January 2020</td><td>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>Corresponding to 8-fold increase in the reporting rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)𝑟 =intrinsic growth rate<em>M</em> = moment generating function</td><td>2.24</td><td>1.96–2.55</td></tr><tr><td>Zhao <em>et al</em>.<span class="xrefLink" id="jumplink-ref7"></span><a href="javascript:;" reveal-id="ref7" data-open="ref7" class="link link-ref link-reveal xref-bibr"><sup>7</sup></a></td><td>China</td><td>10–24 January 2020</td><td>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>Corresponding to 2-fold increase in the reporting rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)𝑟 =intrinsic growth rate<em>M</em> = moment generating function</td><td>3.58</td><td>2.89–4.39</td></tr><tr><td>Imai (2020)<span class="xrefLink" id="jumplink-ref8"></span><a href="javascript:;" reveal-id="ref8" data-open="ref8" class="link link-ref link-reveal xref-bibr"><sup>8</sup></a></td><td>Wuhan</td><td>January 18, 2020</td><td>Mathematical model, computational modelling of potential epidemic trajectories</td><td>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 <em>R</em><sub>0</sub> values for best-case, median and worst-case</td><td>1.5–3.5 (2.5)</td><td>/</td></tr><tr><td>Julien and Althaus<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr"><sup>9</sup></a></td><td>China and overseas</td><td>18 January 2020</td><td>Stochastic simulations of early outbreak trajectories</td><td>Stochastic simulations of early outbreak trajectories were performed that are consistent with the epidemiological findings to date</td><td>2.2</td><td></td></tr><tr><td>Tang <em>et al</em>.<span class="xrefLink" id="jumplink-ref10"></span><a href="javascript:;" reveal-id="ref10" data-open="ref10" class="link link-ref link-reveal xref-bibr"><sup>10</sup></a></td><td>China</td><td>22 January 2020</td><td>Mathematical SEIR-type epidemiological model incorporates appropriate compartments corresponding to interventions</td><td>Method-based method and Likelihood-based method</td><td>6.47</td><td>5.71–7.23</td></tr><tr><td>Qun Li <em>et al</em>.<span class="xrefLink" id="jumplink-ref11"></span><a href="javascript:;" reveal-id="ref11" data-open="ref11" class="link link-ref link-reveal xref-bibr"><sup>11</sup></a></td><td>China</td><td>22 January 2020</td><td>Statistical exponential growth model</td><td>Mean incubation period = 5.2 days, mean serial interval = 7.5 days</td><td>2.2</td><td>1.4–3.9</td></tr><tr><td colspan="5">Averaged</td><td>3.28</td></tr></tbody></table></div><div class="table-modal"><table><thead><tr><td>Study (study year)</td><td>Location</td><td>Study date</td><td>Methods</td><td>Approaches</td><td><em>R</em><sub>0</sub> estimates (average)</td><td>95% CI</td></tr></thead><tbody><tr><td>Joseph <em>et al</em>.<span class="xrefLink" id="jumplink-ref1"></span><a href="javascript:;" reveal-id="ref1" data-open="ref1" class="link link-ref link-reveal xref-bibr"><sup>1</sup></a></td><td>Wuhan</td><td>31 December 2019–28 January 2020</td><td>Stochastic Markov Chain Monte Carlo methods (MCMC)</td><td>MCMC methods with Gibbs sampling and non-informative flat prior, using posterior distribution</td><td>2.68</td><td>2.47–2.86</td></tr><tr><td>Shen <em>et al</em>.<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr"><sup>2</sup></a></td><td>Hubei province</td><td>12–22 January 2020</td><td>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><em>R</em><sub>0</sub> = <span class="inline-formula no-formula-id">|$\beta$|</span>/<span class="inline-formula no-formula-id">|$\alpha$|</span><span class="inline-formula no-formula-id">|$\beta$|</span> = mean person-to-person transmission rate/day in the absence of control interventions, using nonlinear least squares method to get its point estimate<span class="inline-formula no-formula-id">|$\alpha$|</span> = isolation rate = 6</td><td>6.49</td><td>6.31–6.66</td></tr><tr><td>Liu <em>et al</em>.<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr"><sup>3</sup></a></td><td>China and overseas</td><td>23 January 2020</td><td>Statistical exponential Growth, using SARS generation time = 8.4 days, SD = 3.8 days</td><td>Applies Poisson regression to fit the exponential growth rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)<em>M</em> = moment generating function of the generation time distribution<em>r</em> = fitted exponential growth rate</td><td>2.90</td><td>2.32–3.63</td></tr><tr><td>Liu <em>et al</em>.<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr"><sup>3</sup></a></td><td>China and overseas</td><td>23 January 2020</td><td>Statistical maximum likelihood estimation, using SARS generation time = 8.4 days, SD = 3.8 days</td><td>Maximize log-likelihood to estimate <em>R</em><sub>0</sub> by using surveillance data during a disease epidemic, and assuming the secondary case is Poisson distribution with expected value <em>R</em><sub>0</sub></td><td>2.92</td><td>2.28–3.67</td></tr><tr><td>Read <em>et al</em>.<span class="xrefLink" id="jumplink-ref4"></span><a href="javascript:;" reveal-id="ref4" data-open="ref4" class="link link-ref link-reveal xref-bibr"><sup>4</sup></a></td><td>China</td><td>1–22 January 2020</td><td>Mathematical transmission model assuming latent period = 4 days and near to the incubation period</td><td>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>3.11</td><td>2.39–4.13</td></tr><tr><td>Majumder <em>et al</em>.<span class="xrefLink" id="jumplink-ref5"></span><a href="javascript:;" reveal-id="ref5" data-open="ref5" class="link link-ref link-reveal xref-bibr"><sup>5</sup></a></td><td>Wuhan</td><td>8 December 2019 and 26 January 2020</td><td>Mathematical Incidence Decay and Exponential Adjustment (IDEA) model</td><td>Adopted mean serial interval lengths from SARS and MERS ranging from 6 to 10 days to fit the IDEA model,</td><td>2.0–3.1 (2.55)</td><td>/</td></tr><tr><td>WHO</td><td>China</td><td>18 January 2020</td><td>/</td><td>/</td><td>1.4–2.5 (1.95)</td><td>/</td></tr><tr><td>Cao <em>et al</em>.<span class="xrefLink" id="jumplink-ref6"></span><a href="javascript:;" reveal-id="ref6" data-open="ref6" class="link link-ref link-reveal xref-bibr"><sup>6</sup></a></td><td>China</td><td>23 January 2020</td><td>Mathematical model including compartments Susceptible-Exposed-Infectious-Recovered-Death-Cumulative (SEIRDC)</td><td>R = <em>K</em> 2 (<em>L</em> × <em>D</em>) + <em>K</em>(<em>L</em> + <em>D</em>) + 1<em>L</em> = average latent period = 7,<em>D</em> = average latent infectious period = 9,<em>K</em> = logarithmic growth rate of the case counts</td><td>4.08</td><td>/</td></tr><tr><td>Zhao <em>et al</em>.<span class="xrefLink" id="jumplink-ref7"></span><a href="javascript:;" reveal-id="ref7" data-open="ref7" class="link link-ref link-reveal xref-bibr"><sup>7</sup></a></td><td>China</td><td>10–24 January 2020</td><td>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>Corresponding to 8-fold increase in the reporting rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)𝑟 =intrinsic growth rate<em>M</em> = moment generating function</td><td>2.24</td><td>1.96–2.55</td></tr><tr><td>Zhao <em>et al</em>.<span class="xrefLink" id="jumplink-ref7"></span><a href="javascript:;" reveal-id="ref7" data-open="ref7" class="link link-ref link-reveal xref-bibr"><sup>7</sup></a></td><td>China</td><td>10–24 January 2020</td><td>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>Corresponding to 2-fold increase in the reporting rate<em>R</em><sub>0</sub> = 1/<em>M</em>(−𝑟)𝑟 =intrinsic growth rate<em>M</em> = moment generating function</td><td>3.58</td><td>2.89–4.39</td></tr><tr><td>Imai (2020)<span class="xrefLink" id="jumplink-ref8"></span><a href="javascript:;" reveal-id="ref8" data-open="ref8" class="link link-ref link-reveal xref-bibr"><sup>8</sup></a></td><td>Wuhan</td><td>January 18, 2020</td><td>Mathematical model, computational modelling of potential epidemic trajectories</td><td>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 <em>R</em><sub>0</sub> values for best-case, median and worst-case</td><td>1.5–3.5 (2.5)</td><td>/</td></tr><tr><td>Julien and Althaus<span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr"><sup>9</sup></a></td><td>China and overseas</td><td>18 January 2020</td><td>Stochastic simulations of early outbreak trajectories</td><td>Stochastic simulations of early outbreak trajectories were performed that are consistent with the epidemiological findings to date</td><td>2.2</td><td></td></tr><tr><td>Tang <em>et al</em>.<span class="xrefLink" id="jumplink-ref10"></span><a href="javascript:;" reveal-id="ref10" data-open="ref10" class="link link-ref link-reveal xref-bibr"><sup>10</sup></a></td><td>China</td><td>22 January 2020</td><td>Mathematical SEIR-type epidemiological model incorporates appropriate compartments corresponding to interventions</td><td>Method-based method and Likelihood-based method</td><td>6.47</td><td>5.71–7.23</td></tr><tr><td>Qun Li <em>et al</em>.<span class="xrefLink" id="jumplink-ref11"></span><a href="javascript:;" reveal-id="ref11" data-open="ref11" class="link link-ref link-reveal xref-bibr"><sup>11</sup></a></td><td>China</td><td>22 January 2020</td><td>Statistical exponential growth model</td><td>Mean incubation period = 5.2 days, mean serial interval = 7.5 days</td><td>2.2</td><td>1.4–3.9</td></tr><tr><td colspan="5">Averaged</td><td>3.28</td></tr></tbody></table></div><div class="table-wrap-foot"><p class="chapter-para">CI, Confidence interval.</p></div></div></div> <a id="200748296" scrollto-destination="200748296"></a> <div data-id="f1" data-content-id="f1" class="fig fig-section js-fig-section" swap-content-for-modal="true"><div class="graphic-wrap"><img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/jtm/27/2/10.1093_jtm_taaa021/1/m_taaa021f1.jpeg?Expires=1734987931&Signature=o8xNjYqsS4dffPRZmZUlgASwtl0I3-O15emMYMuplRCgCfQOcFFMxB9~e0ykKE~mX1QrW5J2viCLlHVW6fQHNgpVH2pj216mvnCq7PBIxXiFDyGli2kk~hEUbSvdCC8wzuqJPj~L6WI9uo4QavyTGZ6hUwBv7jKfhUm4Pq5weaLv-MfEFamLByvXF73lZXBh2dQa9eOph~dUf5npuTCeyQztYi-UKLN8ZCAnCDQa86ZWhTh8sSyfQd2YQze4nRH0aoHXwDOFMPKihE~ZgohDHyh8R2s8zF7~~YdigVYnAW4ohQGun17dG8dttExVq~Psq~FWn-hBhk5gBOjqdBBd0g__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="Timeline of the R0 estimates for the 2019-nCoV virus in China" data-path-from-xml="taaa021f1.tif" /><div class="graphic-bottom"><div class="label fig-label" id="label-200748296">Figure 1</div><div class="caption fig-caption"><p class="chapter-para">Timeline of the <em>R</em><sub>0</sub> estimates for the 2019-nCoV virus in China</p></div><div class="ajax-articleAbstract-exclude-regex fig-orig original-slide figure-button-wrap"><a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" role="button" aria-describedby="label-200748296" href="/view-large/figure/200748296/taaa021f1.tif" data-path-from-xml="taaa021f1.tif" target="_blank">Open in new tab</a><a class="download-slide" role="button" aria-describedby="label-200748296" data-section="200748296" href="/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/jtm/27/2/10.1093_jtm_taaa021/1/taaa021f1.jpeg?Expires=1734987931&Signature=eQBHcfsnP-65HMKHgUmShASxpCrp910VBNumWvSbChVo4jTgR062qm2oE~fCVkUoX8EOuOwH3pSDhh3xRMOM9NHokZzivJnztc~GgQXwEBSwZvFs~6ULhczcrb64nxXjKEamxPtvbveR8WlEZh~ZF4hslAHhLsCemoBk5Nx4V35fYRTDkX62fmb4rEabhroissM1NqP~PUog8yo29BS~bv2ZKdRF7gxDm~Zd2zV8xlRdzx5Now10Vnmf6KJL3VJoZwItXkx-b8840m-o053hLaaFiMEi6D0hr-H1D3CGAp4Om8-aVjjyjvNzn7oy9-EaAS3XNdRwiC~x8ZclILfJCg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=200748296&ar=5735319&xsltPath=~/UI/app/XSLT&imagename=&siteId=5407" data-path-from-xml="taaa021f1.tif">Download slide</a></div></div></div></div><p class="chapter-para">The first studies initially reported estimates of <em>R</em><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 class="chapter-para">The two studies using stochastic methods to estimate <em>R</em><sub>0</sub>, reported a range of 2.2–2.68 with an average of 2.44.<span class="xrefLink" id="jumplink-ref1"></span><a href="javascript:;" reveal-id="ref1" data-open="ref1" class="link link-ref link-reveal xref-bibr"><sup>1</sup></a><sup>,</sup><span class="xrefLink" id="jumplink-ref9"></span><a href="javascript:;" reveal-id="ref9" data-open="ref9" class="link link-ref link-reveal xref-bibr"><sup>9</sup></a> The six studies using mathematical methods to estimate <em>R</em><sub>0</sub> produced a range from 1.5 to 6.49, with an average of 4.2.<span class="xrefLink" id="jumplink-ref2"></span><a href="javascript:;" reveal-id="ref2" data-open="ref2" class="link link-ref link-reveal xref-bibr"><sup>2</sup></a><sup>,</sup><span class="xrefLink" id="jumplink-ref4 ref5 ref6"></span><a href="javascript:;" reveal-id="ref4 ref5 ref6" data-open="ref4 ref5 ref6" class="link link-ref link-reveal xref-bibr"><sup>4–6</sup></a><sup>,</sup><span class="xrefLink" id="jumplink-ref8"></span><a href="javascript:;" reveal-id="ref8" data-open="ref8" class="link link-ref link-reveal xref-bibr"><sup>8</sup></a><sup>,</sup><span class="xrefLink" id="jumplink-ref10"></span><a href="javascript:;" reveal-id="ref10" data-open="ref10" class="link link-ref link-reveal xref-bibr"><sup>10</sup></a> The three studies using statistical methods such as exponential growth estimated an <em>R</em><sub>0</sub> ranging from 2.2 to 3.58, with an average of 2.67.<span class="xrefLink" id="jumplink-ref3"></span><a href="javascript:;" reveal-id="ref3" data-open="ref3" class="link link-ref link-reveal xref-bibr"><sup>3</sup></a><sup>,</sup><span class="xrefLink" id="jumplink-ref7"></span><a href="javascript:;" reveal-id="ref7" data-open="ref7" class="link link-ref link-reveal xref-bibr"><sup>7</sup></a><sup>,</sup><span class="xrefLink" id="jumplink-ref11"></span><a href="javascript:;" reveal-id="ref11" data-open="ref11" class="link link-ref link-reveal xref-bibr"><sup>11</sup></a></p> <h2 scrollto-destination=200748299 id="200748299" class="section-title js-splitscreen-section-title" data-legacy-id=sec3>Discussion</h2> <p class="chapter-para">Our review found the average <em>R</em><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 <em>R</em><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 <em>R</em><sub>0</sub> values estimated by some the mathematical studies. For example, modelling assumptions may have played a role. In more recent studies, <em>R</em><sub>0</sub> seems to have stabilized at around 2–3. <em>R</em><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 class="chapter-para"><em>R</em><sub>0</sub> estimates for SARS have been reported to range between 2 and 5, which is within the range of the mean <em>R</em><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> <h2 scrollto-destination=200748302 id="200748302" class="section-title js-splitscreen-section-title" data-legacy-id=sec4>Conclusions</h2> <p class="chapter-para">This review found that the estimated mean <em>R</em><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 <em>R</em><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 <em>R</em><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, <em>R</em><sub>0</sub> for COVID-19 is expected to be around 2–3, which is broadly consistent with the WHO estimate.</p> <h2 scrollto-destination=200748304 id="200748304" class="section-title js-splitscreen-section-title" data-legacy-id=sec5>Author contributions</h2> <p class="chapter-para">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> <h2 scrollto-destination=200748306 id="200748306" class="section-title js-splitscreen-section-title" data-legacy-id=sec6>Conflict of interest</h2> <p class="chapter-para">None declared.</p><p class="chapter-para"><strong>Teaser:</strong> Our review found the average <em>R</em><sub>0</sub> for COVID-19 to be 3.28, which exceeds WHO estimates from 1.4 to 2.5.</p> <h2 scrollto-destination=200748310 id="200748310" class="backreferences-title js-splitscreen-backreferences-title" data-legacy-id=bib1>References</h2> <div class="ref-list js-splitview-ref-list"><div content-id="ref1" class="js-splitview-ref-item" data-legacy-id="ref1"><div class="refLink-parent"><span class="refLink"><a name="jumplink-ref1" href="javascript:;" aria-label="jumplink-ref1" data-id=""></a></span></div><div class="ref false"><div id="ref-auto-ref1" class="ref-content " data-id="ref1"><span class="label title-label">1.</span><div class="mixed-citation citation"><p 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