| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T2 |
114-122 |
Sentence |
denotes |
Abstract |
| T3 |
123-133 |
Sentence |
denotes |
Background |
| T4 |
134-424 |
Sentence |
denotes |
Similar to outbreaks of many other infectious diseases, success in controlling the novel 2019 coronavirus infection requires a timely and accurate monitoring of the epidemic, particularly during its early period with rather limited data while the need for information increases explosively. |
| T5 |
426-433 |
Sentence |
denotes |
Methods |
| T6 |
434-593 |
Sentence |
denotes |
In this study, we used a second derivative model to characterize the coronavirus epidemic in China with cumulatively diagnosed cases during the first 2 months. |
| T7 |
594-687 |
Sentence |
denotes |
The analysis was further enhanced by an exponential model with a close-population assumption. |
| T8 |
688-873 |
Sentence |
denotes |
This model was built with the data and used to assess the detection rate during the study period, considering the differences between the true infections, detectable and detected cases. |
| T9 |
875-882 |
Sentence |
denotes |
Results |
| T10 |
883-995 |
Sentence |
denotes |
Results from the second derivative modeling suggest the coronavirus epidemic as nonlinear and chaotic in nature. |
| T11 |
996-1204 |
Sentence |
denotes |
Although it emerged gradually, the epidemic was highly responsive to massive interventions initiated on January 21, 2020, as indicated by results from both second derivative and exponential modeling analyses. |
| T12 |
1205-1278 |
Sentence |
denotes |
The epidemic started to decelerate immediately after the massive actions. |
