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, 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.

The Geographic Context of Racial Disparities in Aggressive Endometrial Cancer Subtypes: Integrating Social and Environmental Aspects to Discern Biological Outcomes

“…Spatial epidemiological cluster detection techniques, Local Moran’s I (LMI) [ 32 ], Gi* [ 33 ], and GeoMEDD [ 34 ] were used to investigate geographic patterns of non-endometrioid subtypes within the CCCC area in ArcMap (v = 10.7.1) [ 35 ]. Local Moran’s I and Gi* have been used widely in detection of cancer clusters [ 19 , 28 , 36 , 37 ], while GeoMEDD is a new approach developed in COVID-19 spatial syndromic surveillance [ 34 ]. Local Moran’s I and Gi* operate on aggregate geographic units, such as census tracts, in this case.…”

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

“…Kernel density estimation (KDE) is used to estimate data densities that do not have parametric statistical behaviors, that is, do not follow normal, binomial, or exponential distributions (Okabe, Satoh, & Sugihara, 2009). • GeoMEDD (Curtis et al, 2020) is a new real-time cluster detection methodology that provides indicators on the spatial evolution of the disease, based on access to various public sources that account for the location and timing of cases.…”

“…The spatial analysis of COVID-19 also highlights important advances in technology for spatial and geographical science research, for example, the development and consolidation of new spatial analysis software such as GeoMEDD (Curtis et al, 2020) and OSMnx (Boeing, 2020); new models for risk estimation (Chatterjee et al, 2020;Mobaied, 2020;O'Sullivan et al, 2020;Sun, Di, et al, 2020); algorithms for the management of spatial big data (Buscema et al, 2020;Fang et al, 2020;Shah & Patel, 2020); new clustering techniques and automated spatial statistics (Curtis et al, 2020;Fang et al, 2020;Melin et al, 2020); effective new forms of COVID-19 mapping on the web (Ghilardi et al, 2020;Graves & He, 2020;Maharjan et al, 2020); novel UAV applications (Okyere et al, 2020;Sahraoui, Korichi, Kerrache, Bilal, & Amadeo, 2020); and utilization of VGI (Hohl et al, 2020;Rossman et al, 2020;Yoneoka et al, 2020), among others.…”

“…access and information dissemination is the other main issue faced by the spatial epidemiology of COVID-19 when studies try to conduct real-time analysis of an ongoing pandemic. It is well known that there are lags in COVID-19 data reporting Curtis et al (2020). propose that COVID-19 data go directly…”

Spatial Video and EpiExplorer: A Field Strategy to Contextualize Enteric Disease Risk in Slum Environments

“…In this paper, we address this localized mapping conundrum by drawing on the types of data analytics required to support a more spatially exploratory approach. During the authors’ spatial response to COVID-19, the power of real-time data analytics and “big data” investigations, especially the use of dashboard style data manipulations, led to a fresh perspective on epidemiological “space-time risk scenarios” [ 18 ]. Though other interactive software is available to interactively explore data [ 19 ], including different dashboard applications, which allow for the inclusion of spatial data [ 20 , 21 , 22 ], none have the location-specific sophistication and flexibility needed to fully leverage the different data types generated here.…”