| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T116 |
0-232 |
Sentence |
denotes |
There are three further models incorporating data from international travels: the models of Imai and coauthors (Imai et al., 2020), of Kucharski et al. (Kucharski et al., 2020) and of Wu and collaborators (Wu, Leung, & Leung, 2020). |
| T117 |
233-345 |
Sentence |
denotes |
In particular, Imai and coworkers (Imai et al., 2020) estimated a reproduction number of 2.6 (uncertainty range: |
| T118 |
346-355 |
Sentence |
denotes |
1.5–3.5). |
| T119 |
356-495 |
Sentence |
denotes |
Depending on the different scenarios and levels of zoonotic exposure, the reproduction number was found to vary from 1.7 to 2.6 to 1.9–4.2. |
| T120 |
496-615 |
Sentence |
denotes |
Depending on the different estimates of generation time, the reproduction number oscillated from 1.3 to 2.7 to 1.7–4.3. |
| T121 |
616-709 |
Sentence |
denotes |
Based on the level of infectiousness, the reproduction number varied in the range of 1.6–2.9. |
| T122 |
710-869 |
Sentence |
denotes |
Finally, assuming that the novel virus would cause more mild-to-moderate cases than the SARS virus, the reproduction number would be 2.0 (uncertainty 1.4–2.3). |
| T123 |
870-1047 |
Sentence |
denotes |
Moreover, authors found that only public health interventions blocking over 60% of transmission would be really effective in controlling and containing the coronavirus outbreak. |
| T124 |
1048-1361 |
Sentence |
denotes |
Partially based on the findings of Imai and coworkers (Imai et al., 2020) and building on a SIR model, Yu (Yu, 2020) has computed a basic reproduction number of 3.5 and has estimated that only a quarantine rate of infectious population higher than 90% would enable to effectively control the coronavirus outbreak. |
| T125 |
1362-1574 |
Sentence |
denotes |
Kucharski and colleagues (Kucharski et al., 2020) designed a stochastic SEIR model, based on the Euler-Maruyama algorithm with a 6-h time-step and with the transmission rate following a geometric Brownian motion. |
| T126 |
1575-1664 |
Sentence |
denotes |
Time-varying reproduction number was estimated using the sequential Monte-Carlo approach. |
| T127 |
1665-1842 |
Sentence |
denotes |
Authors utilized several datasets to overcome the issue of unreliability of some data sources and to provide real-time estimates, relying on the Poisson probability calculation. |
| T128 |
1843-1927 |
Sentence |
denotes |
Transmission was modeled as a random process, fluctuating and varying over the time. |
| T129 |
1928-2187 |
Sentence |
denotes |
Similar to the model of Imai and coworkers (Imai et al., 2020), the risk of transmission and the risk of causing a large outbreak were modeled based on a negative binomial offspring distribution, with incubation and infectious period being Erlang distributed. |
| T130 |
2188-2323 |
Sentence |
denotes |
Median reproduction number was found to oscillate between 1.6 and 2.9 before the introduction and implementation of travel restriction. |
| T131 |
2324-2608 |
Sentence |
denotes |
The study by Wu and collaborators (Wu et al., 2020), based on nowcasting and forecasting approach, estimated a basic reproductive number of 2.68 (95% credible interval or CrI 2.47–2.86) with 75,815 individuals (95% CrI 37,304–130,330) being infected in Wuhan as of January 25th, 2020. |
| T132 |
2609-2680 |
Sentence |
denotes |
The epidemics doubling time was found to be 6.4 days (95% CrI 5.8–7.1). |
| T133 |
2681-2902 |
Sentence |
denotes |
The dynamics transmission model by Shen and coworkers (Shen, Peng, Xiao, & Zhang, 2020) predicted 8042 (95% CI 4199–11,884) infections and 898 (95% CI 368–1429) deaths, with a fatality rate of 11.02% (95% CI 9.26–12.78%). |
| T134 |
2903-3041 |
Sentence |
denotes |
Authors computed a basic reproduction number of 4.71 (95% CI 4.50–4.92), which decreased to 2.08 (95% CI 1.99–2.18) on January 22nd, 2020. |
| T135 |
3042-3178 |
Sentence |
denotes |
Based on these estimates, the pandemics outbreak is expected to significantly decrease within 77 [95% CI 75–80] days from its beginning. |
| T136 |
3179-3413 |
Sentence |
denotes |
Furthermore, authors found that every one-day reduction in the duration of the period from illness/symptom onset to isolation would reduce the peak population size by 72–84% and the cumulative infected cases and deaths both by 68–80%. |
| T137 |
3414-3616 |
Sentence |
denotes |
The study by Majumder and Mandl (Majumder & Mandl, 2020) utilized the “Incidence Decay and Exponential Adjustment” (IDEA) model and led to an estimate of the reproduction number in the range of 2.0–3.1. |
| T138 |
3617-3801 |
Sentence |
denotes |
Finally, Riou and Althaus (Riou & Althaus, 2020), using a stochastic model simulating epidemics trajectories, computed a reproduction number of 2.2 (90% high density interval 1.4–3.8). |
| T139 |
3802-4042 |
Sentence |
denotes |
Using statistical approaches, namely exponential growth and maximum likelihood techniques, Liu and colleagues (Liu et al., 2020) estimated the value of the reproduction number ranging from 2.90 (95% CI 2.32–3.63) to 2.92 (95% CI 2.28–3.67). |
| T140 |
4043-4434 |
Sentence |
denotes |
Zhang and Wang (Zhang & Wang, 2020), employing a Bayesian framework to infer time-calibrated phylogeny from 33 available genomic sequences, found that the time of the most recent common ancestor (MRCA) was December 17th, 2019 (95% highest posterior density interval from December 7th, 2019 to December 23rd, 2019) and that the value of the reproduction number oscillated between 1.1 and 1.6. |
