Selected article for: "growth model and short term"
Author: Roosa, K.; Lee, Y.; Luo, R.; Kirpich, A.; Rothenberg, R.; Hyman, J.M.; Yan, P.; Chowell, G.
Title: Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020
  • Document date: 2020_2_14
    • ID: 0zw3ukpx_9
    Snippet: 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) . The generalized logistic growth model (GLM) extends the simple logistic growth model to accommodate sub-exponential .....
    Document: 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) . 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 ). 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) . We also include a recently developed sub-epidemic wave model that supports complex epidemic trajectories, including multiple peaks (i.e., SARS in Singapore ). In this approach, the observed reported curve is assumed to be the aggregate of multiple underlying sub-epidemics . A detailed description for each of the models is included in the Supplement.

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