Geographic monitoring for early disease detection (GeoMEDD)

Abstract 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, espe...

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Autores principales:	Andrew Curtis, Jayakrishnan Ajayakumar, Jacqueline Curtis, Sarah Mihalik, Maulik Purohit, Zachary Scott, James Muisyo, James Labadorf, Sorapat Vijitakula, Justin Yax, Daniel W. Goldberg
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/34b287547fef41419e94620f284128c3
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spelling	oai:doaj.org-article:34b287547fef41419e94620f284128c32021-12-02T11:43:59ZGeographic monitoring for early disease detection (GeoMEDD)10.1038/s41598-020-78704-52045-2322https://doaj.org/article/34b287547fef41419e94620f284128c32020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78704-5https://doaj.org/toc/2045-2322Abstract 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.Andrew CurtisJayakrishnan AjayakumarJacqueline CurtisSarah MihalikMaulik PurohitZachary ScottJames MuisyoJames LabadorfSorapat VijitakulaJustin YaxDaniel W. GoldbergNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andrew Curtis Jayakrishnan Ajayakumar Jacqueline Curtis Sarah Mihalik Maulik Purohit Zachary Scott James Muisyo James Labadorf Sorapat Vijitakula Justin Yax Daniel W. Goldberg Geographic monitoring for early disease detection (GeoMEDD)
description	Abstract 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	article
author	Andrew Curtis Jayakrishnan Ajayakumar Jacqueline Curtis Sarah Mihalik Maulik Purohit Zachary Scott James Muisyo James Labadorf Sorapat Vijitakula Justin Yax Daniel W. Goldberg
author_facet	Andrew Curtis Jayakrishnan Ajayakumar Jacqueline Curtis Sarah Mihalik Maulik Purohit Zachary Scott James Muisyo James Labadorf Sorapat Vijitakula Justin Yax Daniel W. Goldberg
author_sort	Andrew Curtis
title	Geographic monitoring for early disease detection (GeoMEDD)
title_short	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_sort	geographic monitoring for early disease detection (geomedd)
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/34b287547fef41419e94620f284128c3
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Geographic monitoring for early disease detection (GeoMEDD)

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