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Geographic monitoring for early disease detection (GeoMEDD)
Identifying emergent patterns of coronavirus disease 2019 (COVID-19) at the local level presents a geographic challenge. The need is not only to integrate multiple data streams from different sources, scales, and cadences, but to also identify meaningful spatial patterns in these data, especially in...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728804/ https://www.ncbi.nlm.nih.gov/pubmed/33303896 http://dx.doi.org/10.1038/s41598-020-78704-5 |
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author | Curtis, Andrew Ajayakumar, Jayakrishnan Curtis, Jacqueline Mihalik, Sarah Purohit, Maulik Scott, Zachary Muisyo, James Labadorf, James Vijitakula, Sorapat Yax, Justin Goldberg, Daniel W. |
author_facet | Curtis, Andrew Ajayakumar, Jayakrishnan Curtis, Jacqueline Mihalik, Sarah Purohit, Maulik Scott, Zachary Muisyo, James Labadorf, James Vijitakula, Sorapat Yax, Justin Goldberg, Daniel W. |
author_sort | Curtis, Andrew |
collection | PubMed |
description | Identifying emergent patterns of coronavirus disease 2019 (COVID-19) at the local level presents a geographic challenge. The need is not only to integrate multiple data streams from different sources, scales, and cadences, but to also identify meaningful spatial patterns in these data, especially in vulnerable settings where even small numbers and low rates are important to pinpoint for early intervention. This paper identifies a gap in current analytical approaches and presents a near-real time assessment of emergent disease that can be used to guide a local intervention strategy: Geographic Monitoring for Early Disease Detection (GeoMEDD). Through integration of a spatial database and two types of clustering algorithms, GeoMEDD uses incoming test data to provide multiple spatial and temporal perspectives on an ever changing disease landscape by connecting cases using different spatial and temporal thresholds. GeoMEDD has proven effective in revealing these different types of clusters, as well as the influencers and accelerators that give insight as to why a cluster exists where it does, and why it evolves, leading to the saving of lives through more timely and geographically targeted intervention. |
format | Online Article Text |
id | pubmed-7728804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77288042020-12-14 Geographic monitoring for early disease detection (GeoMEDD) Curtis, Andrew Ajayakumar, Jayakrishnan Curtis, Jacqueline Mihalik, Sarah Purohit, Maulik Scott, Zachary Muisyo, James Labadorf, James Vijitakula, Sorapat Yax, Justin Goldberg, Daniel W. Sci Rep Article Identifying emergent patterns of coronavirus disease 2019 (COVID-19) at the local level presents a geographic challenge. The need is not only to integrate multiple data streams from different sources, scales, and cadences, but to also identify meaningful spatial patterns in these data, especially in vulnerable settings where even small numbers and low rates are important to pinpoint for early intervention. This paper identifies a gap in current analytical approaches and presents a near-real time assessment of emergent disease that can be used to guide a local intervention strategy: Geographic Monitoring for Early Disease Detection (GeoMEDD). Through integration of a spatial database and two types of clustering algorithms, GeoMEDD uses incoming test data to provide multiple spatial and temporal perspectives on an ever changing disease landscape by connecting cases using different spatial and temporal thresholds. GeoMEDD has proven effective in revealing these different types of clusters, as well as the influencers and accelerators that give insight as to why a cluster exists where it does, and why it evolves, leading to the saving of lives through more timely and geographically targeted intervention. Nature Publishing Group UK 2020-12-10 /pmc/articles/PMC7728804/ /pubmed/33303896 http://dx.doi.org/10.1038/s41598-020-78704-5 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Curtis, Andrew Ajayakumar, Jayakrishnan Curtis, Jacqueline Mihalik, Sarah Purohit, Maulik Scott, Zachary Muisyo, James Labadorf, James Vijitakula, Sorapat Yax, Justin Goldberg, Daniel W. Geographic monitoring for early disease detection (GeoMEDD) |
title | Geographic monitoring for early disease detection (GeoMEDD) |
title_full | Geographic monitoring for early disease detection (GeoMEDD) |
title_fullStr | Geographic monitoring for early disease detection (GeoMEDD) |
title_full_unstemmed | Geographic monitoring for early disease detection (GeoMEDD) |
title_short | Geographic monitoring for early disease detection (GeoMEDD) |
title_sort | geographic monitoring for early disease detection (geomedd) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728804/ https://www.ncbi.nlm.nih.gov/pubmed/33303896 http://dx.doi.org/10.1038/s41598-020-78704-5 |
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