Cargando…
Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model
In 2020, Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) Coronavirus, unforeseen pandemic put humanity at big risk and health professionals are facing several kinds of problem due to rapid growth of confirmed cases. That is why some pr...
Autor principal: | Saqib, Mohd |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581693/ https://www.ncbi.nlm.nih.gov/pubmed/34764555 http://dx.doi.org/10.1007/s10489-020-01942-7 |
Ejemplares similares
-
Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting
por: Waheeb, Waddah, et al.
Publicado: (2016) -
Multi-Step Polynomial Regression Method to Model and Forecast Malaria Incidence
por: Chatterjee, Chandrajit, et al.
Publicado: (2009) -
Forecasting of COVID19 per regions using ARIMA models and polynomial functions
por: Hernandez-Matamoros, Andres, et al.
Publicado: (2020) -
Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models
por: da Silva, Flavia Alves, et al.
Publicado: (2021) -
Fractional ridge regression: a fast, interpretable reparameterization of ridge regression
por: Rokem, Ariel, et al.
Publicado: (2020)