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: | , |
---|---|
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 |
_version_ | 1783695709890936832 |
---|---|
author | Osthus, Dave Moran, Kelly R. |
author_facet | Osthus, Dave Moran, Kelly R. |
author_sort | Osthus, Dave |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8137955 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81379552021-06-03 Multiscale influenza forecasting Osthus, Dave Moran, Kelly R. Nat Commun Article 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. Nature Publishing Group UK 2021-05-20 /pmc/articles/PMC8137955/ /pubmed/34016992 http://dx.doi.org/10.1038/s41467-021-23234-5 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Osthus, Dave Moran, Kelly R. Multiscale influenza forecasting |
title | Multiscale influenza forecasting |
title_full | Multiscale influenza forecasting |
title_fullStr | Multiscale influenza forecasting |
title_full_unstemmed | Multiscale influenza forecasting |
title_short | Multiscale influenza forecasting |
title_sort | multiscale influenza forecasting |
topic | Article |
url | 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 |
work_keys_str_mv | AT osthusdave multiscaleinfluenzaforecasting AT morankellyr multiscaleinfluenzaforecasting |