Cargando…

Accurate influenza forecasts using type-specific incidence data for small geographic units

Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Pre...

Descripción completa

Detalles Bibliográficos
Autores principales: Turtle, James, Riley, Pete, Ben-Nun, Michal, Riley, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354478/
https://www.ncbi.nlm.nih.gov/pubmed/34324487
http://dx.doi.org/10.1371/journal.pcbi.1009230
_version_ 1783736601423118336
author Turtle, James
Riley, Pete
Ben-Nun, Michal
Riley, Steven
author_facet Turtle, James
Riley, Pete
Ben-Nun, Michal
Riley, Steven
author_sort Turtle, James
collection PubMed
description Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics.
format Online
Article
Text
id pubmed-8354478
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-83544782021-08-11 Accurate influenza forecasts using type-specific incidence data for small geographic units Turtle, James Riley, Pete Ben-Nun, Michal Riley, Steven PLoS Comput Biol Research Article Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics. Public Library of Science 2021-07-29 /pmc/articles/PMC8354478/ /pubmed/34324487 http://dx.doi.org/10.1371/journal.pcbi.1009230 Text en © 2021 Turtle et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Turtle, James
Riley, Pete
Ben-Nun, Michal
Riley, Steven
Accurate influenza forecasts using type-specific incidence data for small geographic units
title Accurate influenza forecasts using type-specific incidence data for small geographic units
title_full Accurate influenza forecasts using type-specific incidence data for small geographic units
title_fullStr Accurate influenza forecasts using type-specific incidence data for small geographic units
title_full_unstemmed Accurate influenza forecasts using type-specific incidence data for small geographic units
title_short Accurate influenza forecasts using type-specific incidence data for small geographic units
title_sort accurate influenza forecasts using type-specific incidence data for small geographic units
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354478/
https://www.ncbi.nlm.nih.gov/pubmed/34324487
http://dx.doi.org/10.1371/journal.pcbi.1009230
work_keys_str_mv AT turtlejames accurateinfluenzaforecastsusingtypespecificincidencedataforsmallgeographicunits
AT rileypete accurateinfluenzaforecastsusingtypespecificincidencedataforsmallgeographicunits
AT bennunmichal accurateinfluenzaforecastsusingtypespecificincidencedataforsmallgeographicunits
AT rileysteven accurateinfluenzaforecastsusingtypespecificincidencedataforsmallgeographicunits