LitCovid-sentences  

PMC:7102659 / 1766-19085 JSONTXT 13 Projects

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Id Subject Object Predicate Lexical cue
T13 0-12 Sentence denotes Introduction
T14 13-298 Sentence denotes The ongoing outbreak of coronavirus disease 2019 (COVID-19), has claimed 2663 lives, along with 77,658 confirmed cases and 2824 suspected cases in China, as of 24 February 2020 (24:00 GMT+8), according to the National Health Commission of the People's Republic of China (NHCPRC, 2020).
T15 299-637 Sentence denotes The number of deaths associated with COVID-19 greatly exceeds the other two coronaviruses (severe acure respiratory syndrome coronavirus, SARS-CoV, and Middle East respiratory syndrome coronavirus, MERS-CoV), and the outbreak is still ongoing, which posed a huge threat to the global public health and economics (Bogoch et al., 2020, J.T.
T16 638-655 Sentence denotes Wu et al., 2020).
T17 656-853 Sentence denotes The emergence of COVID-19 coincided with the largest annual human migration in the world, i.e., the Spring Festival travel season, which resulted in a rapid national and global spread of the virus.
T18 854-977 Sentence denotes At the early stage of the outbreak, most cases were scattered, and some linked to the Huanan Seafood Wholesale Market (J.T.
T19 978-995 Sentence denotes Wu et al., 2020).
T20 996-1069 Sentence denotes The Chinese government has adopted extreme measures to mitigate outbreak.
T21 1070-1218 Sentence denotes On 23 January 2020, the local government of Wuhan suspended all public traffics within the city, and closed all inbound and outbound transportation.
T22 1219-1332 Sentence denotes Other cities in Hubei province announced similar traffic control measures following Wuhan shortly, see Figure 1 .
T23 1333-1438 Sentence denotes The resumption date in Wuhan remains unclear as of the submission date of this study on 25 February 2020.
T24 1439-1571 Sentence denotes Figure 1 The timeline of the facts of COVID-19 and control measures implemented in Wuhan, China from December 2019 to February 2020.
T25 1572-1669 Sentence denotes The red dots are the events in the COVID-19 outbreak, and the blue dots are the control measures.
T26 1670-1808 Sentence denotes The public panic in face of the ongoing COVID-19 outbreak reminds us the history of the 1918 influenza pandemic in London, United Kingdom.
T27 1809-2030 Sentence denotes Furthermore, its characteristics of mild symptoms in most cases and short serial interval (i.e., 4–5 days) (You et al., 2002; Zhao et al., 2020c) are similar to pandemic influenza, rather than the other two coronaviruses.
T28 2031-2128 Sentence denotes In 1918, a significant proportion of the deaths were from pneumonia followed influenza infection.
T29 2129-2339 Sentence denotes Thus, it might be reasonable to revisit the modelling framework of 1918 influenza pandemic, and in particular, to capture the effects of the individual reaction (to the risk of infection) and government action.
T30 2340-2613 Sentence denotes In (He et al., 2013), the study proposed a model incorporating individual reaction, holiday effects as well as weather conditions (temperature in London, United Kingdom), which successfully captured the multiple-wave feature in the influenza-associated mortality in London.
T31 2614-2852 Sentence denotes In this study, we followed the form of individual reaction and governmental action effects in (He et al., 2013), except for the effects of weather condition due to limited knowledge on weather effects on the transmission of coronaviruses.
T32 2853-3033 Sentence denotes We note that the governmental action, in both 1918 and current time, summarized all measures including holiday extension, city lockdown, hospitalisation and quarantine of patients.
T33 3034-3137 Sentence denotes We presume it will last for the next few months for the moment, and will update later if things change.
T34 3138-3210 Sentence denotes The parameter values may be improved when more information is available.
T35 3211-3386 Sentence denotes We argue that all prevention and control measures may be categorised into two large groups, which are described by either a step function or a response function, respectively.
T36 3387-3505 Sentence denotes We also consider zoonotic transmission period of one month and a huge emigration from Wuhan (35.7% of the population).
T37 3506-3732 Sentence denotes Nevertheless, our model is a preliminary conceptual model, intending to lay a foundation for further modelling studies, but we can easily tune our model so that the outcomes of our model are in line with previous studies (J.T.
T38 3733-3764 Sentence denotes Wu et al., 2020, Mahase, 2020).
T39 3766-3784 Sentence denotes A conceptual model
T40 3785-4111 Sentence denotes We adopt the ‘Susceptible-Exposed-Infectious-Removed’ (SEIR) framework with the total population size N with two extra classes (1) “D” mimicking the public perception of risk regarding the number of severe and critical cases and deaths; and (2) “C” representing the number of cumulative cases (both reported and not reported).
T41 4112-4255 Sentence denotes Let S, E, and I represent the susceptible, exposed and infectious populations and R represent the removed population (i.e., recovered or dead).
T42 4256-4558 Sentence denotes In a recent study (Wu and McGoogan, 2020), Wu and McGoogan found that 81% of cases were of mild symptom (without pneumonia or only mild pneumonia), 14% were severe case with difficulty breathing, and 5% were critical with respiratory failure, septic shock, and/or multiple organ dysfunction or failure.
T43 4559-4630 Sentence denotes We adopt the transmission rate function formulated in He et al. (2013).
T44 4631-4738 Sentence denotes We rename the school term effect as the governmental action effect, since the former belongs to the latter.
T45 4739-4809 Sentence denotes We also assume a period of zoonotic transmission during December 2019.
T46 4810-4958 Sentence denotes We model the zoonotic transmission (denoted as F) as a stepwise function, which takes zero after the shutdown of Huanan seafood market (presumably).
T47 4959-5172 Sentence denotes We then only model the sustained human-to-human transmission of COVID-19 after this date, along with the emigration of 5 million population before Wuhan was officially locked down (South China Morning Post, 2020).
T48 5173-5353 Sentence denotes Thus, a compartmental model is formulated as follows:(1) S'=-β0SFN-β(t)SIN-μS,E'=β0SFN+β(t)SIN-(σ+μ)E,I'=σE-(γ+μ)I,R'=γI-μR,N'=-μN,D'=d   γI-λD,andC'=σE,where(2) β(t)=β0(1−α)1−DNκ.
T49 5354-5619 Sentence denotes The transmission rate, β(t) in Eq. (2), incorporates the impact of governmental action (all actions which can be modelled as a step function), and the decreasing contacts among individuals responding to the proportion of deaths (i.e., the severity of the epidemic).
T50 5620-5704 Sentence denotes We also incorporate the individuals leaving Wuhan before the lock-down in the model.
T51 5705-6034 Sentence denotes We assume (i) the zoonotic cases only make impacts during December 2019 (Huang et al., 2020); (ii) the effect of governmental action starts on 23 January 2020 (in particular, α  = 0.4249 during 23–29 January 2020 and α  = 0.8478 after that); (iii) the emigration from Wuhan starts on 31 December 2019 and ends on 22 January 2020.
T52 6035-6081 Sentence denotes In this outbreak it seems children are spared.
T53 6082-6180 Sentence denotes Only 0.9% cases are from age 15 or less (Guan et al., 2020), while in China, 0–14 years are 17.2%.
T54 6181-6263 Sentence denotes To take this effect into account, we assume 10% of the population are ‘protected’.
T55 6264-6453 Sentence denotes Recent studies showed the serial interval of COVID-19 could be as short as 5 days (Nishiura et al., 2020a), and the median incubation period could be as short as 4 days (Guan et al., 2020).
T56 6454-6524 Sentence denotes These characteristics imply short latent period and infectious period.
T57 6525-6625 Sentence denotes Thus, we adopt a relatively shorter mean latent period (3 days) and mean infectious period (4 days).
T58 6626-6755 Sentence denotes Different from (He et al., 2013), we use the severe cases and deaths in the individual reaction function, instead of deaths only.
T59 6756-6916 Sentence denotes We also increase the intensity of the governmental action such that the model outcomes (increments in cases) largely match the observed, with a reporting ratio.
T60 6917-6999 Sentence denotes Namely only a proportion of the model generated cases will be reported in reality.
T61 7000-7144 Sentence denotes Many evidences and studies, e.g., (Tuite and Fisman, 2020, Zhao et al., 2020a, Zhao et al., 2020b), suggest the reporting ratio is time-varying.
T62 7145-7185 Sentence denotes We summarise our parameters in Table 1 .
T63 7186-7239 Sentence denotes Table 1 Summary table of the parameters in model (1).
T64 7240-7290 Sentence denotes Parameter Notation Value or range Remark Reference
T65 7291-7350 Sentence denotes Number of zoonotic cases F {0, 10} A stepwise function J.T.
T66 7351-7367 Sentence denotes Wu et al. (2020)
T67 7368-7446 Sentence denotes Initial population size N0 14 million Constant South China Morning Post (2020)
T68 7447-7503 Sentence denotes Initial susceptible population S0 0.9N0 Constant Assumed
T69 7504-7576 Sentence denotes Transmission rate β0 {0.5944, 1.68}a (day−1) A stepwise function Assumed
T70 7577-7662 Sentence denotes Governmental action strength α {0,0.4239,0.8478} A stepwise function He et al. (2013)
T71 7663-7719 Sentence denotes Intensity of responds κ 1117.3 Constant He et al. (2013)
T72 7720-7809 Sentence denotes Emigration rate μ {0, 0.0205} (day−1) A stepwise function South China Morning Post (2020)
T73 7810-7855 Sentence denotes Mean latent period σ−1 3 (days) Constant J.T.
T74 7856-7872 Sentence denotes Wu et al. (2020)
T75 7873-7922 Sentence denotes Mean infectious period γ−1 5 (days) Constant J.T.
T76 7923-7939 Sentence denotes Wu et al. (2020)
T77 7940-8002 Sentence denotes Proportion of severe cases d 0.2 Constant Worldometers. (2020)
T78 8003-8077 Sentence denotes Mean duration of public reaction λ−1 11.2 (days) Constant He et al. (2013)
T79 8078-8219 Sentence denotes a It is derived by assuming that the basic reproduction number, R0=β0γ·σσ+μ=2.8 (referring to Imai et al., 2020, Riou and Althaus, 2020, J.T.
T80 8220-8373 Sentence denotes Wu et al., 2020, Zhao et al., 2020a, Zhao et al., 2020b) when α = 0, by using the next generation matrix approach (van den Driessche and Watmough, 2002).
T81 8374-8416 Sentence denotes The time unit is in year if not mentioned.
T82 8418-8431 Sentence denotes Data analyses
T83 8432-8505 Sentence denotes We summarise the officially reported data from Wuhan, China in Figure 2 .
T84 8506-8573 Sentence denotes There is an increasing trend of daily new confirmations and deaths.
T85 8574-8681 Sentence denotes We argue that these data were heavily impacted by availability of medical supplies and health care workers.
T86 8682-8857 Sentence denotes Figure 2 The daily number of (a) cases or (b) deaths, cumulative number of (c) cases or (d) deaths, and the percentage of (e) cases or (f) deaths, of COVID-19 in Wuhan, China.
T87 8858-8927 Sentence denotes In panel (f), the 100% represents the count of deaths or cured cases.
T88 8928-8995 Sentence denotes The official data report was not available before January 15, 2020.
T89 8996-9068 Sentence denotes We fill the missing data before that from several retrospective studies.
T90 9069-9090 Sentence denotes Among them data in R.
T91 9091-9198 Sentence denotes Li et al. (2020) are daily symptom onset records, while those in Liu et al. (2020) are daily confirmations.
T92 9199-9389 Sentence denotes We notice that there is a delay of 14 days between symptom onset and laboratory confirmation of COVID-19 between the two datasets which are largely the same group of patients, see Figure 3 .
T93 9390-9423 Sentence denotes Namely, if we put back data in R.
T94 9424-9498 Sentence denotes Li et al. (2020) by 14 days, it largely matches data in Liu et al. (2020).
T95 9499-9679 Sentence denotes Thus, we assume a proportion of daily cases (reporting rate) will be reported after 14 days since their infectiousness onset (which is generally no later than their symptom onset).
T96 9680-9931 Sentence denotes Figure 3 Comparison between different sources of reported cases: official released data (NHCPRC, 2020) in red, data from Li et al. (denoted as NEJM) (Li et al., 2020) in green, from Liu et al. (denoted as GDCDC) (Liu et al., 2020) in blue, and from P.
T97 9932-9967 Sentence denotes Wu et al. (denoted as Eurosurv) (P.
T98 9968-9995 Sentence denotes Wu et al., 2020) in purple.
T99 9997-10013 Sentence denotes Model simulation
T100 10014-10051 Sentence denotes We show our simulations in Figure 4 .
T101 10052-10187 Sentence denotes Under the naive scenario, we assume governmental action strength α  = 0 and intensity of individual reaction κ  = 0, which is unlikely.
T102 10188-10337 Sentence denotes The second scenario is when we only consider “individual reaction”, both the peak value and the number of cumulative cases are substantially reduced.
T103 10338-10465 Sentence denotes The third scenario is considering both “individual reaction” and “governmental action”, and the reduction becomes even further.
T104 10466-10644 Sentence denotes We highlight the third scenario, as we know the individual reaction and governmental action existed and played important role in previous epidemic and pandemic (He et al., 2013).
T105 10645-10808 Sentence denotes Our third scenario implies that• The total number of zoonotic infections was 145 which corresponds to the reported 41 zoonotic cases with a reporting rate of ≈28%.
T106 10809-10912 Sentence denotes This level is largely in line with estimates of Riou and Althaus (2020), Nishiura et al. (2020), and Q.
T107 10913-10930 Sentence denotes Li et al. (2020).
T108 10931-11076 Sentence denotes • The cumulative number of cases in Wuhan was 4648 by January 18, 2020, which is in line with estimates of other teams (Bogoch et al., 2020, J.T.
T109 11077-11109 Sentence denotes Wu et al., 2020, NCPERET, 2020).
T110 11110-11183 Sentence denotes • The cumulative number of cases in Wuhan was 16,589 by 27 January, 2020.
T111 11184-11222 Sentence denotes Compared with estimates 25,630 (95%CI:
T112 11223-11346 Sentence denotes 12,260–44,440), announced by University of Hong Kong team on 27 January, 2020, our estimate is low but in their the 95% CI.
T113 11347-11425 Sentence denotes • The cumulative infections could be 84,116 in Wuhan by the end of April 2020.
T114 11426-11641 Sentence denotes • We compare simulated and reported numbers, and reconstruct the daily reporting ratio, which shows an improvement from a level of below 10% to around 50% from January 2020 to February 2020 and reflects the reality.
T115 11642-11871 Sentence denotes • Due to adjustment of the reporting policy, i.e., an effort to report all clinical cases accumulated in the past few days/weeks, there are a few days where the number of reported cases are artificially high than simulated cases.
T116 11872-12035 Sentence denotes The reason is that the reported cases in these few days included clinical cases but not laboratory confirmed that are accumulated in the past few days, also weeks.
T117 12036-12524 Sentence denotes Figure 4 (a) Daily new cases with a reporting delay of 14 days under three scenarios: naive (i.e., no action taken) as grey dotted curve, individual reaction regarding to the outbreak as red dashed curve, and individual reaction plus governmental action as green solid curve and reported cases (from official release and (Li et al., 2020) as grey curve with dotes. (b) The reporting ratio between reported cases and estimates when individual reaction and governmental action are involved.
T118 12525-12723 Sentence denotes The main purpose of this work is to propose a conceptual model to address the individual reaction (controlled by κ) and governmental action (controlled by α), as well as time-varying reporting rate.
T119 12724-12872 Sentence denotes We perform a simple sensitivity ity analyses on α and κ in Figure 5 , where we can see that both α and κ are needed to capture the observed pattern.
T120 12873-12977 Sentence denotes In particular, when α is around 0.9 and κ is greater than 110, the simulated largely match the observed.
T121 12978-13019 Sentence denotes Figure 5 Sensitivity analyses on α and κ.
T122 13020-13123 Sentence denotes We simulate the base model with both individual reaction and governmental action while varying α and κ.
T123 13124-13437 Sentence denotes We show model outcome when (a) α = 0.5 (black solid), 0.6 (red dashed), 0.7 (green dotted), 0.8 (blue dash-dotted) and 0.9 (cyan long dashed curve), while κ = 1117.3, when (b) κ = 100 (black solid), 500 (red dashed), 900 (green dotted), 1300 (blue dash-dotted) and 1700 (cyan long dashed curve), while α = 0.8478.
T124 13438-13472 Sentence denotes Grey dots show the reported cases.
T125 13474-13500 Sentence denotes Discussion and conclusions
T126 13501-13557 Sentence denotes We used some parameter estimates from (He et al., 2013).
T127 13558-13731 Sentence denotes The estimates were obtained via fitting a mechanistic model to the observed weekly influenza and pneumonia mortality in England and Wales during the 1918 influenza pandemic.
T128 13732-13788 Sentence denotes Recent studies showed that COVID-19 transmitted rapidly.
T129 13789-13845 Sentence denotes In this regard, it resembles influenza rather than SARS.
T130 13846-14037 Sentence denotes In our 1918 influenza work (He et al., 2013), we built a similar model as we introduced here, and we fitted that model to weekly influenza and pneumonia mortality in 334 administrative units.
T131 14038-14183 Sentence denotes Note that 1918 influenza had an infection-fatality-rate of 2%, which was at the same level of the case-fatality-rate of COVID-19 in Wuhan, China.
T132 14184-14419 Sentence denotes The merit of our model is that we considered some essential elements, including individual behavioural response, governmental actions, zoonotic transmission and emigration of a large proportion of the population in a short time period.
T133 14420-14536 Sentence denotes Meanwhile, our model is relatively simple and our estimates are in line with previous studies (Imai et al., 2020, P.
T134 14537-14554 Sentence denotes Wu et al., 2020).
T135 14555-14636 Sentence denotes Thus, our model should be considered as a baseline model for further improvement.
T136 14637-14687 Sentence denotes We avoid to fit model to data in conventional way.
T137 14688-14770 Sentence denotes Instead, we use a simple model framework to discuss what elements might be needed.
T138 14771-15045 Sentence denotes For instance, in order to achieve a good fitting performance, one obviously needs to include a time-varying report rate (as we reconstructed in Figure 4b), which was caused by the availability of medical supplies, hospital capacities and changing testing/reporting policies.
T139 15046-15166 Sentence denotes Thus it would be challenging given a relatively short time series, and several other unknown parameters to be estimated.
T140 15167-15354 Sentence denotes We employ some parameter estimates from the 1918 influenza pandemic, given the similar characteristics of COVID-19 and influenza (most cases are mild) and the similar level of mitigation.
T141 15355-15550 Sentence denotes Transmission from asymptotically infected cases is reported but the contribution of asymptomatic transmission is unclear (presumably small), which shall be further investigated in future studies.
T142 15551-15624 Sentence denotes In this work, we focused on the transmission of COVID-19 in Wuhan, China.
T143 15625-15782 Sentence denotes Our conceptual framework can be applied to other cities/countries, or be built into one multiple-patch model for modelling multiple cities/countries context.
T144 15783-15886 Sentence denotes Our model can be fitted to daily data when more information (e.g., daily number of tests) is available.
T145 15888-15930 Sentence denotes Ethics approval and consent to participate
T146 15931-16043 Sentence denotes Since no individual patient's data was collected, the ethical approval or individual consent was not applicable.
T147 16045-16079 Sentence denotes Availability of data and materials
T148 16080-16112 Sentence denotes All data are publicly available.
T149 16114-16121 Sentence denotes Funding
T150 16122-16426 Sentence denotes This research was supported by National Natural Science Foundation of China (Grant number 61672013 and 11601336), H uaian Key Laboratory for Infectious Diseases Control and Prevention (HAP201704), and General Research Fund (Grant Number 15205119) of the Research Grants Council (RGC) of Hong Kong, China.
T151 16428-16438 Sentence denotes Disclaimer
T152 16439-16683 Sentence denotes The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
T153 16685-16707 Sentence denotes Authors’ contributions
T154 16708-16726 Sentence denotes Conceptualization:
T155 16727-16881 Sentence denotes Qianying Lin, Shi Zhao, Daozhou Gao, Yijun Lou, Salihu S Musa, Shu Yang, Maggie H Wang, Yongli Cai, Weiming Wang, Lin Yang and Daihai He; Formal analysis:
T156 16882-17022 Sentence denotes Qianying Lin, Shi Zhao, Daozhou Gao, Yijun Lou, Salihu S Musa, Shu Yang, Maggie H Wang, Weiming Wang, Lin Yang and Daihai He; Visualization:
T157 17023-17058 Sentence denotes Lin Yang; Writing – original draft:
T158 17059-17237 Sentence denotes Qianying Lin, Shi Zhao, Daozhou Gao, Yijun Lou, Salihu S Musa, Shu Yang, Maggie H Wang, Yongli Cai, Weiming Wang and Lin Yang; Writing – review & editing, Lin Yang and Daihai He.
T159 17239-17260 Sentence denotes Conflict of interests
T160 17261-17319 Sentence denotes The authors declare that they have no competing interests.