文章基本信息

标题：Geographic monitoring for early disease detection (GeoMEDD)
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作者：Andrew Curtis ; Jayakrishnan Ajayakumar ; Jacqueline Curtis 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2020
卷号：10
期号：1
页码：1-11
DOI：10.1038/s41598-020-78704-5
出版社：Springer Nature
摘要：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.
其他摘要：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.