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An analecta of visualizations for foodborne illness trends and seasonality

Disease surveillance systems worldwide face increasing pressure to maintain and distribute data in usable formats supplemented with effective visualizations to enable actionable policy and programming responses. Annual reports and interactive portals provide access to surveillance data and visualiza...

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Autores principales: Simpson, Ryan B., Zhou, Bingjie, Alarcon Falconi, Tania M., Naumova, Elena N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553952/
https://www.ncbi.nlm.nih.gov/pubmed/33051470
http://dx.doi.org/10.1038/s41597-020-00677-x
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author Simpson, Ryan B.
Zhou, Bingjie
Alarcon Falconi, Tania M.
Naumova, Elena N.
author_facet Simpson, Ryan B.
Zhou, Bingjie
Alarcon Falconi, Tania M.
Naumova, Elena N.
author_sort Simpson, Ryan B.
collection PubMed
description Disease surveillance systems worldwide face increasing pressure to maintain and distribute data in usable formats supplemented with effective visualizations to enable actionable policy and programming responses. Annual reports and interactive portals provide access to surveillance data and visualizations depicting temporal trends and seasonal patterns of diseases. Analyses and visuals are typically limited to reporting the annual time series and the month with the highest number of cases per year. Yet, detecting potential disease outbreaks and supporting public health interventions requires detailed spatiotemporal comparisons to characterize spatiotemporal patterns of illness across diseases and locations. The Centers for Disease Control and Prevention’s (CDC) FoodNet Fast provides population-based foodborne-disease surveillance records and visualizations for select counties across the US. We offer suggestions on how current FoodNet Fast data organization and visual analytics can be improved to facilitate data interpretation, decision-making, and communication of features related to trend and seasonality. The resulting compilation, or analecta, of 436 visualizations of records and codes are openly available online.
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spelling pubmed-75539522020-10-19 An analecta of visualizations for foodborne illness trends and seasonality Simpson, Ryan B. Zhou, Bingjie Alarcon Falconi, Tania M. Naumova, Elena N. Sci Data Analysis Disease surveillance systems worldwide face increasing pressure to maintain and distribute data in usable formats supplemented with effective visualizations to enable actionable policy and programming responses. Annual reports and interactive portals provide access to surveillance data and visualizations depicting temporal trends and seasonal patterns of diseases. Analyses and visuals are typically limited to reporting the annual time series and the month with the highest number of cases per year. Yet, detecting potential disease outbreaks and supporting public health interventions requires detailed spatiotemporal comparisons to characterize spatiotemporal patterns of illness across diseases and locations. The Centers for Disease Control and Prevention’s (CDC) FoodNet Fast provides population-based foodborne-disease surveillance records and visualizations for select counties across the US. We offer suggestions on how current FoodNet Fast data organization and visual analytics can be improved to facilitate data interpretation, decision-making, and communication of features related to trend and seasonality. The resulting compilation, or analecta, of 436 visualizations of records and codes are openly available online. Nature Publishing Group UK 2020-10-13 /pmc/articles/PMC7553952/ /pubmed/33051470 http://dx.doi.org/10.1038/s41597-020-00677-x Text en © The Author(s) 2020 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/.
spellingShingle Analysis
Simpson, Ryan B.
Zhou, Bingjie
Alarcon Falconi, Tania M.
Naumova, Elena N.
An analecta of visualizations for foodborne illness trends and seasonality
title An analecta of visualizations for foodborne illness trends and seasonality
title_full An analecta of visualizations for foodborne illness trends and seasonality
title_fullStr An analecta of visualizations for foodborne illness trends and seasonality
title_full_unstemmed An analecta of visualizations for foodborne illness trends and seasonality
title_short An analecta of visualizations for foodborne illness trends and seasonality
title_sort analecta of visualizations for foodborne illness trends and seasonality
topic Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553952/
https://www.ncbi.nlm.nih.gov/pubmed/33051470
http://dx.doi.org/10.1038/s41597-020-00677-x
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