| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T118 |
0-20 |
Sentence |
denotes |
Parameter estimation |
| T119 |
21-96 |
Sentence |
denotes |
The parameters were estimated based on the following facts and assumptions: |
| T120 |
97-167 |
Sentence |
denotes |
The mean incubation period was 5.2 days (95% confidence interval [CI]: |
| T121 |
168-181 |
Sentence |
denotes |
4.1–7.0) [3]. |
| T122 |
182-276 |
Sentence |
denotes |
We set the same value (5.2 days) of the incubation period and the latent period in this study. |
| T123 |
277-301 |
Sentence |
denotes |
Thus, ωP = ω’P = 0.1923. |
| T124 |
302-500 |
Sentence |
denotes |
There is a mean 5-day delay from symptom onset to detection/hospitalization of a case (the cases detected in Thailand and Japan were hospitalized from 3 to 7 days after onset, respectively) [19–21]. |
| T125 |
501-661 |
Sentence |
denotes |
The duration from illness onset to first medical visit for the 45 patients with illness onset before January 1 was estimated to have a mean of 5.8 days (95% CI: |
| T126 |
662-675 |
Sentence |
denotes |
4.3–7.5) [3]. |
| T127 |
676-744 |
Sentence |
denotes |
In our model, we set the infectious period of the cases as 5.8 days. |
| T128 |
745-768 |
Sentence |
denotes |
Therefore, γP = 0.1724. |
| T129 |
769-915 |
Sentence |
denotes |
Since there was no data on the proportion of asymptomatic infection of the virus, we simulated the baseline value of proportion of 0.5 (δP = 0.5). |
| T130 |
916-1162 |
Sentence |
denotes |
Since there was no evidence about the transmissibility of asymptomatic infection, we assumed that the transmissibility of asymptomatic infection was 0.5 times that of symptomatic infection (κ = 0.5), which was the similar value as influenza [22]. |
| T131 |
1163-1235 |
Sentence |
denotes |
We assumed that the relative shedding rate of AP compared to IP was 0.5. |
| T132 |
1236-1250 |
Sentence |
denotes |
Thus, c = 0.5. |
| T133 |
1251-1444 |
Sentence |
denotes |
Since 14 January, 2020, Wuhan City has strengthened the body temperature detection of passengers leaving Wuhan at airports, railway stations, long-distance bus stations and passenger terminals. |
| T134 |
1445-1542 |
Sentence |
denotes |
As of January 17, a total of nearly 0.3 million people had been tested for body temperature [23]. |
| T135 |
1543-1605 |
Sentence |
denotes |
In Wuhan, there are about 2.87 million mobile population [24]. |
| T136 |
1606-1847 |
Sentence |
denotes |
We assumed that there was 0.1 million people moving out to Wuhan City per day since January 10, 2020, and we believe that this number would increase (mainly due to the winter vacation and the Chinese New Year holiday) until 24 January, 2020. |
| T137 |
1848-1929 |
Sentence |
denotes |
This means that the 2.87 million would move out from Wuhan City in about 14 days. |
| T138 |
1930-2002 |
Sentence |
denotes |
Therefore, we set the moving volume of 0.2 million per day in our model. |
| T139 |
2003-2160 |
Sentence |
denotes |
Since the population of Wuhan was about 11 million at the end of 2018 [25], the rate of people traveling out from Wuhan City would be 0.018 (0.2/11) per day. |
| T140 |
2161-2273 |
Sentence |
denotes |
However, we assumed that the normal population mobility before January 1 was 0.1 times as that after January 10. |
| T141 |
2274-2391 |
Sentence |
denotes |
Therefore, we set the rate of people moving into and moving out from Wuhan City as 0.0018 per day (nP = mP = 0.0018). |
| T142 |
2392-2477 |
Sentence |
denotes |
The parameters bP and bW were estimated by fitting the model with the collected data. |
| T143 |
2478-2585 |
Sentence |
denotes |
At the beginning of the simulation, we assumed that the prevalence of the virus in the market was 1/100000. |
| T144 |
2586-2777 |
Sentence |
denotes |
Since the SARS-CoV-2 is an RNA virus, we assumed that it could be died in the environment in a short time, but it could be stay for a longer time (10 days) in the unknown hosts in the market. |
| T145 |
2778-2793 |
Sentence |
denotes |
We set ε = 0.1. |
