| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T37 |
0-7 |
Sentence |
denotes |
Methods |
| T38 |
9-13 |
Sentence |
denotes |
Data |
| T39 |
14-236 |
Sentence |
denotes |
We obtained daily updates of the cumulative number of reported confirmed cases for the 2019-nCoV epidemic across provinces in China from the National Health Commission of China website (Chinese National Health Commission). |
| T40 |
237-413 |
Sentence |
denotes |
The data contains 34 areas, including provinces, municipalities, autonomous regions, and special administrative regions; here we refer to the regions collectively as provinces. |
| T41 |
414-514 |
Sentence |
denotes |
Data updates were collected daily at 12 p.m. (GMT-5), between January 22, 2020 and February 9, 2020. |
| T42 |
515-680 |
Sentence |
denotes |
The short time-series is affected by irregularities and reporting lags, so the cumulative curves are more stable and likely yield more stable and reliable estimates. |
| T43 |
681-865 |
Sentence |
denotes |
Therefore, we analyze the cumulative trajectory of the epidemic in Hubei province, the epicenter of the outbreak, as well as the cumulative aggregate trajectory of all other provinces. |
| T44 |
867-873 |
Sentence |
denotes |
Models |
| T45 |
874-1206 |
Sentence |
denotes |
We generate short-term forecasts in real-time using three phenomenological models that have been previously used to derive short-term forecasts for a number of epidemics for several infectious diseases, including SARS, Ebola, pandemic influenza, and dengue (Chowell, Tariq, & Hyman, 2019; Pell et al., 2018; Wang, Wu, & Yang, 2012). |
| T46 |
1207-1413 |
Sentence |
denotes |
The generalized logistic growth model (GLM) extends the simple logistic growth model to accommodate sub-exponential growth dynamics with a scaling of growth parameter, p (Viboud, Simonsen, & Chowell, 2016). |
| T47 |
1414-1579 |
Sentence |
denotes |
The Richards model also includes a scaling parameter, a, to allow for deviation from the symmetric logistic curve (Chowell, 2017; Richards, 1959; Wang et al., 2012). |
| T48 |
1580-1760 |
Sentence |
denotes |
We also include a recently developed sub-epidemic wave model that supports complex epidemic trajectories, including multiple peaks (i.e., SARS in Singapore (Chowell et al., 2019)). |
| T49 |
1761-1898 |
Sentence |
denotes |
In this approach, the observed reported curve is assumed to be the aggregate of multiple underlying sub-epidemics (Chowell et al., 2019). |
| T50 |
1899-1975 |
Sentence |
denotes |
A detailed description for each of the models is included in the Supplement. |
| T51 |
1977-1997 |
Sentence |
denotes |
Short-term forecasts |
| T52 |
1998-2116 |
Sentence |
denotes |
We calibrate each model to the daily cumulative reported case counts for Hubei and other provinces (all except Hubei). |
| T53 |
2117-2244 |
Sentence |
denotes |
While the outbreak began in December 2019, available data on cumulative case counts are available starting on January 22, 2020. |
| T54 |
2245-2354 |
Sentence |
denotes |
Therefore, the first calibration process includes 15 observations: from January 22, 2020 to February 5, 2020. |
| T55 |
2355-2543 |
Sentence |
denotes |
Each subsequent calibration period increases by one day with each new published daily data, with the last calibration period between January 22, 2020 and February 9, 2020 (19 data points). |
| T56 |
2544-2643 |
Sentence |
denotes |
We estimate the best-fit model solution to the reported data using nonlinear least squares fitting. |
| T57 |
2644-3026 |
Sentence |
denotes |
This process yields the set of model parameters Θ that minimizes the sum of squared errors between the model f(t,Θ) and the data yt; where ΘGLM = (r, p, K), ΘRich = (r, a, K), and ΘSub = (r, p, K 0 , q, C thr) correspond to the estimated parameter sets for the GLM, the Richards model, and the sub-epidemic model, respectively; parameter descriptions are provided in the Supplement. |
| T58 |
3027-3122 |
Sentence |
denotes |
Thus, the best-fit solution f(t,Θˆ) is defined by the parameter set Θˆ=argmin∑t=1n(f(t,Θ)−yt)2. |
| T59 |
3123-3176 |
Sentence |
denotes |
We fix the initial condition to the first data point. |
| T60 |
3177-3310 |
Sentence |
denotes |
We then use a parametric bootstrap approach to quantify uncertainty around the best-fit solution, assuming a Poisson error structure. |
| T61 |
3311-3417 |
Sentence |
denotes |
A detailed description of this method is provided in prior studies (Chowell, 2017; Roosa & Chowell, 2019). |
| T62 |
3418-3573 |
Sentence |
denotes |
The models are refitted to the M = 200 bootstrap datasets to obtain M parameter sets, which are used to define 95% confidence intervals for each parameter. |
| T63 |
3574-3716 |
Sentence |
denotes |
Each of the M model solutions to the bootstrap curves is used to generate m = 30 simulations extended through a forecasting period of 15 days. |
| T64 |
3717-3800 |
Sentence |
denotes |
These 6000 (M × m) curves construct the 95% prediction intervals for the forecasts. |
