Cargando…
Multiscale influenza forecasting
Influenza forecasting in the United States (US) is complex and challenging due to spatial and temporal variability, nested geographic scales of interest, and heterogeneous surveillance participation. Here we present Dante, a multiscale influenza forecasting model that learns rather than prescribes s...
Autores principales: | Osthus, Dave, Moran, Kelly R. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137955/ https://www.ncbi.nlm.nih.gov/pubmed/34016992 http://dx.doi.org/10.1038/s41467-021-23234-5 |
Ejemplares similares
-
Fast and accurate influenza forecasting in the United States with Inferno
por: Osthus, Dave
Publicado: (2022) -
Improving probabilistic infectious disease forecasting through coherence
por: Gibson, Graham Casey, et al.
Publicado: (2021) -
Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited
por: Osthus, Dave, et al.
Publicado: (2019) -
Addressing delayed case reporting in infectious disease forecast modeling
por: Beesley, Lauren J., et al.
Publicado: (2022) -
A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States
por: Reich, Nicholas G., et al.
Publicado: (2019)