| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T677 |
0-144 |
Sentence |
denotes |
This paper examines the transmission dynamics of the coronavirus disease 2019 in China, considering both within- and between-city transmissions. |
| T678 |
145-375 |
Sentence |
denotes |
Our sample is from January 19 to February 29 and covers key episodes such as the initial spread of the virus across China, the peak of infections in terms of domestic case counts, and the gradual containment of the virus in China. |
| T679 |
376-536 |
Sentence |
denotes |
Changes in weather conditions induce exogenous variations in past infection rates, which allow us to identify the causal impact of past infections on new cases. |
| T680 |
537-698 |
Sentence |
denotes |
The estimates suggest that the infectious effect of the existing cases is mostly observed within 1 week and people’s responses can break the chain of infections. |
| T681 |
699-871 |
Sentence |
denotes |
Comparing estimates in two sub-samples, we observe that the spread of COVID-19 has been effectively contained by mid February, especially for cities outside Hubei province. |
| T682 |
872-960 |
Sentence |
denotes |
Data on real-time population flows between cities have become available in recent years. |
| T683 |
961-1149 |
Sentence |
denotes |
We show that this new source of data is valuable in explaining between-city transmissions of COVID-19, even after controlling for traditional measures of geographic and economic proximity. |
