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Id Subject Object Predicate Lexical cue
T1 0-74 Sentence denotes The reproductive number of COVID-19 is higher compared to SARS coronavirus
T2 76-88 Sentence denotes Introduction
T3 89-202 Sentence denotes In Wuhan, China, a novel and alarmingly contagious primary atypical (viral) pneumonia broke out in December 2019.
T4 203-327 Sentence denotes It has since been identified as a zoonotic coronavirus, similar to SARS coronavirus and MERS coronavirus and named COVID-19.
T5 328-417 Sentence denotes As of 8 February 2020, 33 738 confirmed cases and 811 deaths have been reported in China.
T6 418-490 Sentence denotes Here we review the basic reproduction number (R0) of the COVID-19 virus.
T7 491-661 Sentence denotes R0 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.
T8 662-767 Sentence denotes For R0 > 1, the number infected is likely to increase, and for R0 < 1, transmission is likely to die out.
T9 768-930 Sentence denotes The basic reproduction number is a central concept in infectious disease epidemiology, indicating the risk of an infectious agent with respect to epidemic spread.
T10 932-951 Sentence denotes Methods and Results
T11 952-1032 Sentence denotes PubMed, bioRxiv and Google Scholar were accessed to search for eligible studies.
T12 1033-1093 Sentence denotes The term ‘coronavirus & basic reproduction number’ was used.
T13 1094-1161 Sentence denotes The time period covered was from 1 January 2020 to 7 February 2020.
T14 1162-1292 Sentence denotes For this time period, we identified 12 studies which estimated the basic reproductive number for COVID-19 from China and overseas.
T15 1293-1427 Sentence denotes Table 1 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.
T16 1428-1476 Sentence denotes Table 1 Published estimates of R0 for 2019-nCoV
T17 1477-1572 Sentence denotes Study (study year) Location Study date Methods Approaches R 0 estimates (average) 95% CI
T18 1573-1792 Sentence denotes Joseph et al.1 Wuhan 31 December 2019–28 January 2020 Stochastic Markov Chain Monte Carlo methods (MCMC) MCMC methods with Gibbs sampling and non-informative flat prior, using posterior distribution 2.68 2.47–2.86
T19 1793-3330 Sentence denotes Shen et al.2 Hubei province 12–22 January 2020 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 R 0 = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta$\end{document}/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\alpha$\end{document}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta$\end{document} = mean person-to-person transmission rate/day in the absence of control interventions, using nonlinear least squares method to get its point estimate\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\alpha$\end{document} = isolation rate = 6 6.49 6.31–6.66
T20 3331-3657 Sentence denotes Liu et al.3 China and overseas 23 January 2020 Statistical exponential Growth, using SARS generation time = 8.4 days, SD = 3.8 days Applies Poisson regression to fit the exponential growth rateR0 = 1/M(−𝑟)M = moment generating function of the generation time distributionr = fitted exponential growth rate 2.90 2.32–3.63
T21 3658-3993 Sentence denotes Liu et al.3 China and overseas 23 January 2020 Statistical maximum likelihood estimation, using SARS generation time = 8.4 days, SD = 3.8 days Maximize log-likelihood to estimate R0 by using surveillance data during a disease epidemic, and assuming the secondary case is Poisson distribution with expected value R0 2.92 2.28–3.67
T22 3994-4326 Sentence denotes Read et al.4 China 1–22 January 2020 Mathematical transmission model assuming latent period = 4 days and near to the incubation period Assumes daily time increments with Poisson-distribution and apply a deterministic SEIR metapopulation transmission model, transmission rate = 1.94, infectious period =1.61 days 3.11 2.39–4.13
T23 4327-4582 Sentence denotes Majumder et al.5 Wuhan 8 December 2019 and 26 January 2020 Mathematical Incidence Decay and Exponential Adjustment (IDEA) model Adopted mean serial interval lengths from SARS and MERS ranging from 6 to 10 days to fit the IDEA model, 2.0–3.1 (2.55) /
T24 4583-4635 Sentence denotes WHO China 18 January 2020 / / 1.4–2.5 (1.95) /
T25 4636-4939 Sentence denotes Cao et al.6 China 23 January 2020 Mathematical model including compartments Susceptible-Exposed-Infectious-Recovered-Death-Cumulative (SEIRDC) R = K 2 (L × D) + K(L + D) + 1L = average latent period = 7,D = average latent infectious period = 9,K = logarithmic growth rate of the case counts 4.08 /
T26 4940-5273 Sentence denotes Zhao et al.7 China 10–24 January 2020 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) Corresponding to 8-fold increase in the reporting rateR0 = 1/M(−𝑟)𝑟 =intrinsic growth rateM = moment generating function 2.24 1.96–2.55
T27 5274-5607 Sentence denotes Zhao et al.7 China 10–24 January 2020 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) Corresponding to 2-fold increase in the reporting rateR0 = 1/M(−𝑟)𝑟 =intrinsic growth rateM = moment generating function 3.58 2.89–4.39
T28 5608-6022 Sentence denotes Imai (2020)8 Wuhan January 18, 2020 Mathematical model, computational modelling of potential epidemic trajectories 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 R0 values for best-case, median and worst-case 1.5–3.5 (2.5) /
T29 6023-6271 Sentence denotes Julien and Althaus9 China and overseas 18 January 2020 Stochastic simulations of early outbreak trajectories Stochastic simulations of early outbreak trajectories were performed that are consistent with the epidemiological findings to date 2.2
T30 6272-6490 Sentence denotes Tang et al.10 China 22 January 2020 Mathematical SEIR-type epidemiological model incorporates appropriate compartments corresponding to interventions Method-based method and Likelihood-based method 6.47 5.71–7.23
T31 6491-6650 Sentence denotes Qun Li et al.11 China 22 January 2020 Statistical exponential growth model Mean incubation period = 5.2 days, mean serial interval = 7.5 days 2.2 1.4–3.9
T32 6651-6665 Sentence denotes Averaged 3.28
T33 6666-6690 Sentence denotes CI, Confidence interval.
T34 6691-6834 Sentence denotes Figure 1 Timeline of the R0 estimates for the 2019-nCoV virus in China The first studies initially reported estimates of R0 with lower values.
T35 6835-6967 Sentence denotes Estimations subsequently increased and then again returned in the most recent estimates to the levels initially reported (Figure 1).
T36 6968-7036 Sentence denotes A closer look reveals that the estimation method used played a role.
T37 7037-7421 Sentence denotes The two studies using stochastic methods to estimate R0, reported a range of 2.2–2.68 with an average of 2.44.1,9 The six studies using mathematical methods to estimate R0 produced a range from 1.5 to 6.49, with an average of 4.2.2,4–6,8,10 The three studies using statistical methods such as exponential growth estimated an R0 ranging from 2.2 to 3.58, with an average of 2.67.3,7,11
T38 7423-7433 Sentence denotes Discussion
T39 7434-7543 Sentence denotes Our review found the average R0 to be 3.28 and median to be 2.79, which exceed WHO estimates from 1.4 to 2.5.
T40 7544-7662 Sentence denotes The studies using stochastic and statistical methods for deriving R0 provide estimates that are reasonably comparable.
T41 7663-7758 Sentence denotes However, the studies using mathematical methods produce estimates that are, on average, higher.
T42 7759-7876 Sentence denotes Some of the mathematically derived estimates fall within the range produced the statistical and stochastic estimates.
T43 7877-7990 Sentence denotes It is important to further assess the reason for the higher R0 values estimated by some the mathematical studies.
T44 7991-8049 Sentence denotes For example, modelling assumptions may have played a role.
T45 8050-8116 Sentence denotes In more recent studies, R0 seems to have stabilized at around 2–3.
T46 8117-8281 Sentence denotes R0 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.
T47 8282-8475 Sentence denotes 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.
T48 8476-8619 Sentence denotes R 0 estimates for SARS have been reported to range between 2 and 5, which is within the range of the mean R0 for COVID-19 found in this review.
T49 8620-8698 Sentence denotes Due to similarities of both pathogen and region of exposure, this is expected.
T50 8699-8898 Sentence denotes 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.
T51 8900-8911 Sentence denotes Conclusions
T52 8912-9084 Sentence denotes This review found that the estimated mean R0 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.
T53 9085-9198 Sentence denotes These estimates of R0 depend on the estimation method used as well as the validity of the underlying assumptions.
T54 9199-9303 Sentence denotes Due to insufficient data and short onset time, current estimates of R0 for COVID-19 are possibly biased.
T55 9304-9422 Sentence denotes However, as more data are accumulated, estimation error can be expected to decrease and a clearer picture should form.
T56 9423-9550 Sentence denotes Based on these considerations, R0 for COVID-19 is expected to be around 2–3, which is broadly consistent with the WHO estimate.
T57 9552-9572 Sentence denotes Author contributions
T58 9573-9671 Sentence denotes J.R. and A.W.S. had the idea, and Y.L. did the literature search and created the table and figure.
T59 9672-9747 Sentence denotes Y.L. and A.W.S. wrote the first draft; A.A.G. drafted the final manuscript.
T60 9748-9796 Sentence denotes All authors contributed to the final manuscript.
T61 9798-9818 Sentence denotes Conflict of interest
T62 9819-9833 Sentence denotes None declared.
T63 9836-9843 Sentence denotes Teaser:
T64 9844-9945 Sentence denotes Our review found the average R0 for COVID-19 to be 3.28, which exceeds WHO estimates from 1.4 to 2.5.