Geographic monitoring for early disease detection (GeoMEDD)

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...

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Bibliographic Details
Published in:Scientific reports Vol. 10; no. 1; p. 21753
Main Authors: Curtis, Andrew, Ajayakumar, Jayakrishnan, Curtis, Jacqueline, Mihalik, Sarah, Purohit, Maulik, Scott, Zachary, Muisyo, James, Labadorf, James, Vijitakula, Sorapat, Yax, Justin, Goldberg, Daniel W.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 10-12-2020
Nature Publishing Group
Nature Portfolio
Subjects:
692/699
692/700
Algorithms
Coronaviruses
COVID-19
COVID-19 - epidemiology
Databases, Factual
Disease detection
Epidemiological Monitoring
Geographic Information Systems
Humanities and Social Sciences
Humans
multidisciplinary
Pandemics
SARS-CoV-2
Science
Science (multidisciplinary)
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-78704-5