Cargando…
Inference on the dynamics of COVID-19 in the United States
The evolution of the COVID-19 pandemic is described through a time-dependent stochastic dynamic model in discrete time. The proposed multi-compartment model is expressed through a system of difference equations. Information on the social distancing measures and diagnostic testing rates are incorpora...
Autores principales: | Bhattacharjee, Satarupa, Liao, Shuting, Paul, Debashis, Chaudhuri, Sanjay |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831615/ https://www.ncbi.nlm.nih.gov/pubmed/35145115 http://dx.doi.org/10.1038/s41598-021-04494-z |
Ejemplares similares
-
Learning delay dynamics for multivariate stochastic processes, with application to the prediction of the growth rate of COVID-19 cases in the United States()
por: Dubey, Paromita, et al.
Publicado: (2022) -
Time dynamics of COVID-19
por: Carroll, Cody, et al.
Publicado: (2020) -
Bayesian Inference of State-Level COVID-19 Basic Reproduction Numbers across the United States
por: Mallela, Abhishek, et al.
Publicado: (2022) -
Successful Treatment of Acute Myeloid Leukemia with CPX-351 in 2 Patients in the United Kingdom during the COVID-19 Pandemic
por: Munisamy, Sreetharan, et al.
Publicado: (2021) -
Raga todi intervention on state anxiety level in female young adults during COVID-19
por: Deka, Satarupa, et al.
Publicado: (2022)