Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread

Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to qua...

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Autores principales: Bhatia, Sangeeta, Lassmann, Britta, Cohn, Emily, Desai, Angel N., Carrion, Malwina, Kraemer, Moritz U. G., Herringer, Mark, Brownstein, John, Madoff, Larry, Cori, Anne, Nouvellet, Pierre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
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Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8052406/
https://www.ncbi.nlm.nih.gov/pubmed/33864009
http://dx.doi.org/10.1038/s41746-021-00442-3
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author Bhatia, Sangeeta
Lassmann, Britta
Cohn, Emily
Desai, Angel N.
Carrion, Malwina
Kraemer, Moritz U. G.
Herringer, Mark
Brownstein, John
Madoff, Larry
Cori, Anne
Nouvellet, Pierre
author_facet Bhatia, Sangeeta
Lassmann, Britta
Cohn, Emily
Desai, Angel N.
Carrion, Malwina
Kraemer, Moritz U. G.
Herringer, Mark
Brownstein, John
Madoff, Larry
Cori, Anne
Nouvellet, Pierre
author_sort Bhatia, Sangeeta
collection PubMed
description Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.
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spelling pubmed-80524062021-05-05 Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread Bhatia, Sangeeta Lassmann, Britta Cohn, Emily Desai, Angel N. Carrion, Malwina Kraemer, Moritz U. G. Herringer, Mark Brownstein, John Madoff, Larry Cori, Anne Nouvellet, Pierre NPJ Digit Med Article Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak. Nature Publishing Group UK 2021-04-16 /pmc/articles/PMC8052406/ /pubmed/33864009 http://dx.doi.org/10.1038/s41746-021-00442-3 Text en © The Author(s) 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
Bhatia, Sangeeta
Lassmann, Britta
Cohn, Emily
Desai, Angel N.
Carrion, Malwina
Kraemer, Moritz U. G.
Herringer, Mark
Brownstein, John
Madoff, Larry
Cori, Anne
Nouvellet, Pierre
Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread
title Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread
title_full Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread
title_fullStr Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread
title_full_unstemmed Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread
title_short Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread
title_sort using digital surveillance tools for near real-time mapping of the risk of infectious disease spread
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8052406/
https://www.ncbi.nlm.nih.gov/pubmed/33864009
http://dx.doi.org/10.1038/s41746-021-00442-3
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