LitCovid-sentences  

PMC:7033348 / 6675-10475 JSONTXT 9 Projects

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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.