| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T86 |
0-123 |
Sentence |
denotes |
To model the spread of the virus, we consider within-city spread and between-city transmissions simultaneously (Adda 2016). |
| T87 |
124-340 |
Sentence |
denotes |
Our starting point is yct=∑s=114αwithin,syc,t−s+∑s=114αbetween,s∑r≠cdcr−1yr,t−s+∑s=114ρszt−s+xctβ+𝜖ct, where c is a city other than Wuhan, and yct is the number of new confirmed cases of COVID-19 in city c on date t. |
| T88 |
341-522 |
Sentence |
denotes |
Regarding between-city transmissions, dcr is the log of the distance between cities c and r, and ∑r≠cdcr−1yrt is the inverse distance weighted sum of new infections in other cities. |
| T89 |
523-823 |
Sentence |
denotes |
Considering that COVID-19 epidemic originated from one city (Wuhan) and that most of the early cases outside Wuhan can be traced to previous contacts with persons in Wuhan, we also include the number of new confirmed cases in Wuhan (zt) to model how the virus spreads to other cities from its source. |
| T90 |
824-1087 |
Sentence |
denotes |
We may include lagged yct, yrt, and zt up to 14 days based on the estimates of the durations of the infectious period and the incubation period in the literature7. xct includes contemporaneous weather controls, city, and day fixed effects8. 𝜖ct is the error term. |
| T91 |
1088-1130 |
Sentence |
denotes |
Standard errors are clustered by province. |
