Explaining COVID-19 outbreaks with reactive SEIRD models
Abstract COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our lack of understanding of how the pandemic has evolved leads to increasing errors i...
Guardado en:
Autores principales: | Kunal Menda, Lucas Laird, Mykel J. Kochenderfer, Rajmonda S. Caceres |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/4af6e01c390d4777963f86cf0d7b3f10 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics
por: Maíra Aguiar, et al.
Publicado: (2021) -
A simple stochastic model with environmental transmission explains multi-year periodicity in outbreaks of avian flu.
por: Rong-Hua Wang, et al.
Publicado: (2012) -
Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model
por: Yuan Zhang, et al.
Publicado: (2020) -
Transmission dynamics and control measures of COVID-19 outbreak in China: a modelling study
por: Xu-Sheng Zhang, et al.
Publicado: (2021) -
Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China
por: Wei Liu, et al.
Publicado: (2021)