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.
Search related documents:
Co phrase search for related documents- infectious disease and logistic growth model: 1, 2, 3, 4, 5, 6
- infectious disease and multiple peak: 1, 2
- infectious disease and pandemic influenza: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74
- infectious disease and phenomenological model: 1, 2, 3, 4
- infectious disease and real time: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76
- infectious disease and richards model: 1, 2, 3
- infectious disease and scale parameter: 1
- infectious disease and short term: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
- infectious disease and sub epidemic: 1, 2, 3, 4, 5, 6
- logistic curve and real time: 1
- logistic curve and richards model: 1, 2, 3
- logistic curve and short term: 1, 2, 3
- logistic curve and short term forecast: 1
- logistic curve and symmetric logistic curve: 1, 2, 3
- logistic growth model and real time: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
- logistic growth model and sub epidemic: 1, 2, 3, 4, 5, 6
- logistic growth model and sub epidemic wave: 1, 2, 3, 4
- logistic growth model and sub epidemic wave model: 1, 2, 3, 4
- logistic growth model and symmetric logistic curve: 1
Co phrase search for related documents, hyperlinks ordered by date