LitCovid-sentences  

PMC:7001239 / 663-14223 JSONTXT 13 Projects

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Id Subject Object Predicate Lexical cue
T7 0-153 Sentence denotes On 31 December 2019, the World Health Organization (WHO) was alerted about a cluster of pneumonia of unknown aetiology in the city of Wuhan, China [1,2].
T8 154-301 Sentence denotes Only a few days later, Chinese authorities identified and characterised a novel coronavirus (2019-nCoV) as the causative agent of the outbreak [3].
T9 302-585 Sentence denotes The outbreak appears to have started from a single or multiple zoonotic transmission events at a wet market in Wuhan where game animals and meat were sold [4] and has resulted in 5,997 confirmed cases in China and 68 confirmed cases in several other countries by 29 January 2020 [5].
T10 586-737 Sentence denotes Based on the number of exported cases identified in other countries, the actual size of the epidemic in Wuhan has been estimated to be much larger [6].
T11 738-914 Sentence denotes At this early stage of the outbreak, it is important to gain understanding of the transmission pattern and the potential for sustained human-to-human transmission of 2019-nCoV.
T12 915-1219 Sentence denotes Information on the transmission characteristics will help coordinate current screening and containment strategies, support decision making on whether the outbreak constitutes a public health emergency of international concern (PHEIC), and is key for anticipating the risk of pandemic spread of 2019-nCoV.
T13 1220-1424 Sentence denotes In order to better understand the early transmission pattern of 2019-nCoV, we performed stochastic simulations of early outbreak trajectories that are consistent with the epidemiological findings to date.
T14 1426-1445 Sentence denotes Epidemic parameters
T15 1446-1508 Sentence denotes Two key properties will determine further spread of 2019-nCoV.
T16 1509-1727 Sentence denotes Firstly, the basic reproduction number R0 describes the average number of secondary cases generated by an infectious index case in a fully susceptible population, as was the case during the early phase of the outbreak.
T17 1728-1857 Sentence denotes If R0 is above the critical threshold of 1, continuous human-to-human transmission with sustained transmission chains will occur.
T18 1858-2042 Sentence denotes Secondly, the individual variation in the number of secondary cases provides further information about the expected outbreak dynamics and the potential for superspreading events [7-9].
T19 2043-2264 Sentence denotes If the dispersion of the number of secondary cases is high, a small number of cases may be responsible for a disproportionate number of secondary cases, while a large number of cases will not transmit the pathogen at all.
T20 2265-2426 Sentence denotes While superspreading always remain a rare event, it can result in a large and explosive transmission event and have a lot of impact on the course of an epidemic.
T21 2427-2573 Sentence denotes Conversely, low dispersion would lead to a steadier growth of the epidemic, with more homogeneity in the number of secondary cases per index case.
T22 2574-2626 Sentence denotes This has important implications for control efforts.
T23 2628-2666 Sentence denotes Simulating early outbreak trajectories
T24 2667-2731 Sentence denotes In a first step, we initialised simulations with one index case.
T25 2732-2876 Sentence denotes For each primary case, we generated secondary cases according to a negative-binomial offspring distribution with mean R0 and dispersion k [7,8].
T26 2877-3108 Sentence denotes The dispersion parameter k quantifies the variability in the number of secondary cases, and can be interpreted as a measure of the impact of superspreading events (the lower the value of k, the higher the impact of superspreading).
T27 3109-3250 Sentence denotes The generation time interval D was assumed to be gamma-distributed with a shape parameter of 2, and a mean that varied between 7 and 14 days.
T28 3251-3379 Sentence denotes We explored a wide range of parameter combinations (Table) and ran 1,000 stochastic simulations for each individual combination.
T29 3380-3583 Sentence denotes This corresponds to a total of 3.52 million one-index-case simulations that were run on UBELIX (http://www.id.unibe.ch/hpc), the high performance computing cluster at the University of Bern, Switzerland.
T30 3584-3710 Sentence denotes Table Parameter ranges for stochastic simulations of outbreak trajectories, 2019 novel coronavirus outbreak, China, 2019–2020
T31 3711-3784 Sentence denotes Parameter Description Range Number of values explored within the range
T32 3785-3841 Sentence denotes R0 Basic reproduction number 0.8–5.0 22 (equidistant)
T33 3842-3906 Sentence denotes k Dispersion parameter 0.0110 20 (equidistant on log10 scale)
T34 3907-3973 Sentence denotes D Generation time interval (days) 9–11,13,16–19 8 (equidistant)
T35 3974-4029 Sentence denotes n Initial number of index cases 1–50 6 (equidistant)
T36 4030-4299 Sentence denotes T Date of zoonotic transmission 20 Nov–4 Dec 2019 Randomised for each index case In a second step, we accounted for the uncertainty regarding the number of index cases n and the date T of the initial zoonotic animal-to-human transmissions at the wet market in Wuhan.
T37 4300-4432 Sentence denotes An epidemic with several index cases can be considered as the aggregation of several independent epidemics with one index case each.
T38 4433-4586 Sentence denotes We sampled (with replacement) n of the one-index-case epidemics, sampled a date of onset for each index case and aggregated the epidemic curves together.
T39 4587-4763 Sentence denotes The sampling of the date of onset was done uniformly from a 2-week interval around 27 November 2019, in coherence with early phylogenetic analyses of 11 2019-nCoV genomes [10].
T40 4764-4976 Sentence denotes This step was repeated 100 times for each combination of R0 (22 points), k (20 points), D (8 points) and n (6 points) for a total of 2,112,000 full epidemics simulated that included the uncertainty on D, n and T.
T41 4977-5183 Sentence denotes Finally, we calculated the proportion of stochastic simulations that reached a total number of infected cases within the interval between 1,000 and 9,700 by 18 January 2020, as estimated by Imai et al. [6].
T42 5184-5439 Sentence denotes In a process related to approximate Bayesian computation (ABC), the parameter value combinations that led to simulations within that interval were treated as approximations to the posterior distributions of the parameters with uniform prior distributions.
T43 5440-5535 Sentence denotes Model simulations and analyses were performed in the R software for statistical computing [11].
T44 5536-5594 Sentence denotes Code files are available on https://github.com/jriou/wcov.
T45 5596-5654 Sentence denotes Transmission characteristics of the 2019 novel coronavirus
T46 5655-5874 Sentence denotes In order to reach between 1,000 and 9,700 infected cases by 18 January 2020, the early human-to-human transmission of 2019-nCoV was characterised by values of R0 around 2.2 (median value, with 90% high density interval:
T47 5875-5895 Sentence denotes 1.4–3.8) (Figure 1).
T48 5896-6011 Sentence denotes The observed data at this point are compatible with a large range of values for the dispersion parameter k (median:
T49 6012-6044 Sentence denotes 0.54, 90% high density interval:
T50 6045-6057 Sentence denotes 0.014–6.95).
T51 6058-6133 Sentence denotes However, our simulations suggest that very low values of k are less likely.
T52 6134-6297 Sentence denotes These estimates incorporate the uncertainty about the total epidemic size on 18 January 2020 and about the date and scale of the initial zoonotic event (Figure 2).
T53 6298-6434 Sentence denotes Figure 1 Values of R0 and k most compatible with the estimated size of the 2019 novel coronavirus epidemic in China, on 18 January 2020
T54 6435-6507 Sentence denotes The basic reproduction number R0 quantifies human-to-human transmission.
T55 6508-6654 Sentence denotes The dispersion parameter k quantifies the risk of a superspreading event (lower values of k are linked to a higher probability of superspreading).
T56 6655-6725 Sentence denotes Note that the probability density of k implies a log10 transformation.
T57 6726-6826 Sentence denotes Figure 2 Illustration of the simulation strategy, 2019 novel coronavirus outbreak, China, 2019–2020
T58 6827-6918 Sentence denotes The lines represent the cumulative incidence of 480 simulations with R0 = 1.8 and k = 1.13.
T59 6919-6980 Sentence denotes The other parameters are left to vary according to the Table.
T60 6981-7102 Sentence denotes Among these simulated epidemics, 54.3% led to a cumulative incidence between 1,000 and 9,700 on 18 January 2020 (in red).
T61 7104-7158 Sentence denotes Comparison with past emergences of respiratory viruses
T62 7159-7322 Sentence denotes Comparison with other emerging coronaviruses in the past allows to put into perspective the available information regarding the transmission patterns of 2019-nCoV.
T63 7323-7418 Sentence denotes Figure 3 shows the combinations of R0 and k that are most likely at this stage of the epidemic.
T64 7419-7647 Sentence denotes Our estimates of R0 and k are more similar to previous estimates focusing on early human-to-human transmission of SARS-CoV in Beijing and Singapore [7] than of Middle East respiratory syndrome-related coronavirus (MERS-CoV) [9].
T65 7648-7870 Sentence denotes The spread of MERS-CoV was characterised by small clusters of transmission following repeated instances of animal-to-human transmission events, mainly driven by the occurrence of superspreading events in hospital settings.
T66 7871-7964 Sentence denotes MERS-CoV could however not sustain human-to-human transmission beyond a few generations [12].
T67 7965-8129 Sentence denotes Conversely, the international spread of SARS-CoV lasted for 9 months and was driven by sustained human-to-human transmission, with occasional superspreading events.
T68 8130-8260 Sentence denotes It led to more than 8,000 cases around the world and required extensive efforts by public health authorities to be contained [13].
T69 8261-8367 Sentence denotes Our assessment of the early transmission of 2019-nCoV suggests that 2019-nCoV might follow a similar path.
T70 8368-8537 Sentence denotes Figure 3 Proportion of simulated epidemics that lead to a cumulative incidence between 1,000 and 9,700 of the 2019 novel coronavirus outbreak, China, on 18 January 2020
T71 8538-8543 Sentence denotes MERS:
T72 8544-8658 Sentence denotes Middle East respiratory syndrome-related coronavirus; SARS: severe acute respiratory syndrome-related coronavirus.
T73 8659-8820 Sentence denotes This can be interpreted as the combinations of R0 and k values most compatible with the estimation of epidemic size before quarantine measures were put in place.
T74 8821-8991 Sentence denotes As a comparison, we show the estimates of R0 and k for the early human-to-human transmission of SARS-CoV in Singapore and Beijing and of 1918 pandemic influenza [7,9,14].
T75 8992-9110 Sentence denotes Our estimates for 2019-nCoV are also compatible with those of 1918 pandemic influenza, for which k was estimated [14].
T76 9111-9292 Sentence denotes Human-to-human transmission of influenza viruses is characterised by R0 values between 1.5 and 2 and a larger value of k, implying a more steady transmission without superspreading.
T77 9293-9511 Sentence denotes The emergence of new strains of influenza, for which human populations carried little to no immunity contrary to seasonal influenza, led to pandemics with different severity such as the ones in1918, 1957 1968 and 2009.
T78 9512-9749 Sentence denotes It is notable that coronaviruses differ from influenza viruses in many aspects, and evidence for the 2019-nCoV with respect to case fatality rate, transmissibility from asymptomatic individuals and speed of transmission is still limited.
T79 9750-10011 Sentence denotes Without speculating about possible consequences, the values of R0 and k found here during the early stage of 2019-nCoV emergence and the lack of immunity to 2019-nCoV in the human population leave open the possibility for pandemic circulation of this new virus.
T80 10013-10038 Sentence denotes Strengths and limitations
T81 10039-10261 Sentence denotes The scarcity of available data, especially on case counts by date of disease onset as well as contact tracing, greatly limits the precision of our estimates and does not yet allow for reliable forecasts of epidemic spread.
T82 10262-10425 Sentence denotes Case counts provided by local authorities in the early stage of an emerging epidemic are notoriously unreliable as reporting rates are unstable and vary with time.
T83 10426-10574 Sentence denotes This is due to many factors such as the initial lack of proper diagnosis tools, the focus on the more severe cases or the overcrowding of hospitals.
T84 10575-10781 Sentence denotes We avoided this surveillance bias by relying on an indirect estimate of epidemic size on 18 January, based on cases identified in foreign countries before quarantine measures were implemented on 23 January.
T85 10782-10965 Sentence denotes This estimated range of epidemic size relies itself on several assumptions, including that all infected individuals who travelled from Wuhan to other countries have been detected [6].
T86 10966-11101 Sentence denotes This caveat may lead to an underestimation of transmissibility, especially considering the recent reports about asymptomatic cases [4].
T87 11102-11315 Sentence denotes Conversely, our results do not depend on any assumption about the existence of asymptomatic transmission, and only reflect the possible combinations of transmission events that lead to the situation on 18 January.
T88 11316-11405 Sentence denotes Our analysis, while limited because of the scarcity of data, has two important strengths.
T89 11406-11852 Sentence denotes Firstly, it is based on the simulation of a wide range of possibilities regarding epidemic parameters and allows for the full propagation on the final estimates of the many remaining uncertainties regarding 2019-nCoV and the situation in Wuhan: on the actual size of the epidemic, on the size of the initial zoonotic event at the wet market, on the date(s) of the initial animal-to-human transmission event(s) and on the generation time interval.
T90 11853-12018 Sentence denotes As it accounts for all these uncertainties, our analysis provides a summary of the current state of knowledge about the human-to-human transmissibility of 2019-nCoV.
T91 12019-12198 Sentence denotes Secondly, its focus on the possibility of superspreading events by using negative-binomial offspring distributions appears relevant in the context of emerging coronaviruses [7,8].
T92 12199-12398 Sentence denotes While our estimate of k remains imprecise, the simulations suggest that very low values of k < 0.1 are less likely than higher values < 0.1 that correspond to a more homogeneous transmission pattern.
T93 12399-12579 Sentence denotes However, values of k in the range of 0.1–0.2 are still compatible with a small risk of occurrence of large superspreading events, especially impactful in hospital settings [15,16].
T94 12581-12592 Sentence denotes Conclusions
T95 12593-12727 Sentence denotes Our analysis suggests that the early pattern of human-to-human transmission of 2019-nCoV is reminiscent of SARS-CoV emergence in 2002.
T96 12728-12833 Sentence denotes International collaboration and coordination will be crucial in order to contain the spread of 2019-nCoV.
T97 12834-13053 Sentence denotes At this stage, particular attention should be given to the prevention of possible rare but explosive superspreading events, while the establishment of sustained transmission chains from single cases cannot be ruled out.
T98 13054-13278 Sentence denotes The previous experience with SARS-CoV has shown that established practices of infection control, such as early detection and isolation, contact tracing and the use of personal protective equipment, can stop such an epidemic.
T99 13279-13560 Sentence denotes Given the existing uncertainty around the case fatality rate and transmission, our findings confirm the importance of screening, surveillance and control efforts, particularly at airports and other transportation hubs, in order to prevent further international spread of 2019-nCoV.