Multiscale influenza forecasting.
Nat Commun
; 12(1): 2991, 2021 05 20.
Article
en En
| MEDLINE
| ID: mdl-34016992
ABSTRACT
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 spatial, temporal, and surveillance data structure and generates coherent forecasts across state, regional, and national scales. We retrospectively compare Dante's short-term and seasonal forecasts for previous flu seasons to the Dynamic Bayesian Model (DBM), a leading competitor. Dante outperformed DBM for nearly all spatial units, flu seasons, geographic scales, and forecasting targets. Dante's sharper and more accurate forecasts also suggest greater public health utility. Dante placed 1st in the Centers for Disease Control and Prevention's prospective 2018/19 FluSight challenge in both the national and regional competition and the state competition. The methodology underpinning Dante can be used in other seasonal disease forecasting contexts having nested geographic scales of interest.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Gripe Humana
/
Epidemias
/
Monitoreo Epidemiológico
/
Predicción
Tipo de estudio:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Límite:
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos